From 08463c5d0b6f216c1fcedc6e698130219ebca2ca Mon Sep 17 00:00:00 2001 From: Daniel Tomlinson Date: Sat, 20 Jul 2019 23:21:32 +0100 Subject: [PATCH] completed part 2 implementing gradient descent --- .../__pycache__/data_prep.cpython-37.pyc | Bin 0 -> 835 bytes .../Backpropagation/backprop.py | 48 ++ .../Backpropagation/backprop1.py | 78 +++ .../Backpropagation/binary.csv | 401 ++++++++++++++++ .../Backpropagation/data_prep.py | 22 + .../Multilayer Perceptron/data_prep.py | 24 + .../Multilayer Perceptron/gradient.py | 56 +++ .../Multilayer Perceptron/gradient_2.py | 71 +++ .../Multilayer Perceptron/multilayer.py | 38 ++ .../Single Perceptron/binary.csv | 401 ++++++++++++++++ .../Single Perceptron/data_prep.py | 24 + .../Single Perceptron/gradient_2.py | 71 +++ .../__pycache__/data_prep.cpython-37.pyc | Bin 0 -> 823 bytes .../finding_donors-checkpoint.ipynb | 450 ++++++++++-------- .../Project/finding_donors.ipynb | 412 ++++++++-------- 15 files changed, 1685 insertions(+), 411 deletions(-) create mode 100644 python/Deep Learning/Implementing Gradient Descent/Backpropagation/__pycache__/data_prep.cpython-37.pyc create mode 100644 python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop1.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Backpropagation/binary.csv create mode 100644 python/Deep Learning/Implementing Gradient Descent/Backpropagation/data_prep.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/data_prep.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient_2.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/multilayer.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Single Perceptron/binary.csv create mode 100644 python/Deep Learning/Implementing Gradient Descent/Single Perceptron/data_prep.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/Single Perceptron/gradient_2.py create mode 100644 python/Deep Learning/Implementing Gradient Descent/__pycache__/data_prep.cpython-37.pyc diff --git a/python/Deep Learning/Implementing Gradient Descent/Backpropagation/__pycache__/data_prep.cpython-37.pyc b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/__pycache__/data_prep.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eefe20778de23307dcec3382a5324700764ec1ae GIT binary patch literal 835 zcmX|8&1%~~5Z;w!TbAwg&vESpis;RUMu%QQTY_l`w4qQ+VIi2UW^At`tyHaC>|9Ha zc?X-G`Y!DY?6s#rpP*3a$Z&`{$)BG8br}0qCAU_m;01lgsa{)*1#PwaKXEBrViAge&q-s{dtpWyKFgv#)zFCivWEL z25jBhuwB&NFo%hjXy4@6>N9K~Qg_|~4*aWw!NfUcpUyro1tZ{zu0n!1Pg z@d5f@I5fMAd;qO0S9GbfkKF;`tee=~aN_VqZ@X3dW-IieJ*c=Jiaz#e+uM3?S09K2 z;P2S5b6sh3b{TwBS{AFL*nGBLA@#+O+j?H$L@lpLD@Lg&m&y!DsA+-Za(TT^jp@%H zmHmGHa_Csks7ths0`o-1C~6W?RSpkrO)t`XWgVT1dM-6cW1Sp?U!a6BQHr3NG)86G z*m|7lSeBwS#c~XbG*xJ<2eOpHgCfgqZK5y%>!m2QbxjG@O|sb5X>G_-S^^OWWtOQJ zZB1#wrFE4qX$-Zk8=2+_T1PEKvqVE21a18Z%5qT_ev zqT6rXpKsADOEH2nOB1C{rlUAp6b4Q79%QV_Rrp56Go>+njk3^6Ps1nSsV<8Q7I8_J z4$q{{07+O3-xk@lkZF{!${8Vz5cB`PMW@y3DeAJqcvVPH6o(@;F`dzgj4yIhNKR#i s8dVn?(I)az6K+zzzAH8}I}E*lqKW_uM}J2Qhv6NB{r; literal 0 HcmV?d00001 diff --git a/python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop.py b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop.py new file mode 100644 index 0000000..1c2a53f --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop.py @@ -0,0 +1,48 @@ +import numpy as np + + +def sigmoid(x): + """ + Calculate sigmoid + """ + return 1 / (1 + np.exp(-x)) + + +x = np.array([0.5, 0.1, -0.2]) +target = 0.6 +learnrate = 0.5 + +weights_input_hidden = np.array([[0.5, -0.6], + [0.1, -0.2], + [0.1, 0.7]]) + +weights_hidden_output = np.array([0.1, -0.3]) + +# Forward pass +hidden_layer_input = np.dot(x, weights_input_hidden) +hidden_layer_output = sigmoid(hidden_layer_input) + +output_layer_in = np.dot(hidden_layer_output, weights_hidden_output) +output = sigmoid(output_layer_in) + +# Backwards pass +# TODO: Calculate output error +error = target - output + +# TODO: Calculate error term for output layer +output_error_term = error * output * (1 - output) + +# TODO: Calculate error term for hidden layer +hidden_error_term = np.dot(output_error_term, weights_hidden_output + * hidden_layer_output * (1 - hidden_layer_output)) + +# TODO: Calculate change in weights for hidden layer to output layer +delta_w_h_o = learnrate * output_error_term * hidden_layer_output + +# TODO: Calculate change in weights for input layer to hidden layer +delta_w_i_h = learnrate * hidden_error_term * x[:, None] + +print('Change in weights for hidden layer to output layer:') +print(delta_w_h_o) +print('Change in weights for input layer to hidden layer:') +print(delta_w_i_h) diff --git a/python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop1.py b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop1.py new file mode 100644 index 0000000..6f7d707 --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/backprop1.py @@ -0,0 +1,78 @@ +import numpy as np +from data_prep import features, targets, features_test, targets_test + +np.random.seed(21) + + +def sigmoid(x): + """ + Calculate sigmoid + """ + return 1 / (1 + np.exp(-x)) + + +# Hyperparameters +n_hidden = 2 # number of hidden units +epochs = 900 +learnrate = 0.005 + +n_records, n_features = features.shape +last_loss = None +# Initialize weights +weights_input_hidden = np.random.normal(scale=1 / n_features ** .5, + size=(n_features, n_hidden)) +weights_hidden_output = np.random.normal(scale=1 / n_features ** .5, + size=n_hidden) + +for e in range(epochs): + del_w_input_hidden = np.zeros(weights_input_hidden.shape) + del_w_hidden_output = np.zeros(weights_hidden_output.shape) + for x, y in zip(features.values, targets): + ## Forward pass ## + # TODO: Calculate the output + hidden_input = np.dot(x, weights_input_hidden) + hidden_output = sigmoid(hidden_input) + output = sigmoid(np.dot(hidden_output, weights_hidden_output)) + + ## Backward pass ## + # TODO: Calculate the network's prediction error + error = y - output + + # TODO: Calculate error term for the output unit + output_error_term = error * output * (1 - output) + + # propagate errors to hidden layer + + # TODO: Calculate the hidden layer's contribution to the error + hidden_error = np.dot(output_error_term, weights_hidden_output) + + # TODO: Calculate the error term for the hidden layer + hidden_error_term = hidden_error * hidden_output * (1 - hidden_output) + + # TODO: Update the change in weights + del_w_hidden_output += output_error_term * hidden_output + del_w_input_hidden += hidden_error_term * x[:, None] + + # TODO: Update weights (don't forget to division by n_records or number of samples) + weights_input_hidden += learnrate * del_w_input_hidden / n_records + weights_hidden_output += learnrate * del_w_hidden_output / n_records + + # Printing out the mean square error on the training set + if e % (epochs / 10) == 0: + hidden_output = sigmoid(np.dot(x, weights_input_hidden)) + out = sigmoid(np.dot(hidden_output, + weights_hidden_output)) + loss = np.mean((out - targets) ** 2) + + if last_loss and last_loss < loss: + print("Train loss: ", loss, " WARNING - Loss Increasing") + else: + print("Train loss: ", loss) + last_loss = loss + +# Calculate accuracy on test data +hidden = sigmoid(np.dot(features_test, weights_input_hidden)) +out = sigmoid(np.dot(hidden, weights_hidden_output)) +predictions = out > 0.5 +accuracy = np.mean(predictions == targets_test) +print("Prediction accuracy: {:.3f}".format(accuracy)) diff --git a/python/Deep Learning/Implementing Gradient Descent/Backpropagation/binary.csv b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/binary.csv new file mode 100644 index 0000000..5f2cf4e --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/binary.csv @@ -0,0 +1,401 @@ +admit,gre,gpa,rank +0,380,3.61,3 +1,660,3.67,3 +1,800,4,1 +1,640,3.19,4 +0,520,2.93,4 +1,760,3,2 +1,560,2.98,1 +0,400,3.08,2 +1,540,3.39,3 +0,700,3.92,2 +0,800,4,4 +0,440,3.22,1 +1,760,4,1 +0,700,3.08,2 +1,700,4,1 +0,480,3.44,3 +0,780,3.87,4 +0,360,2.56,3 +0,800,3.75,2 +1,540,3.81,1 +0,500,3.17,3 +1,660,3.63,2 +0,600,2.82,4 +0,680,3.19,4 +1,760,3.35,2 +1,800,3.66,1 +1,620,3.61,1 +1,520,3.74,4 +1,780,3.22,2 +0,520,3.29,1 +0,540,3.78,4 +0,760,3.35,3 +0,600,3.4,3 +1,800,4,3 +0,360,3.14,1 +0,400,3.05,2 +0,580,3.25,1 +0,520,2.9,3 +1,500,3.13,2 +1,520,2.68,3 +0,560,2.42,2 +1,580,3.32,2 +1,600,3.15,2 +0,500,3.31,3 +0,700,2.94,2 +1,460,3.45,3 +1,580,3.46,2 +0,500,2.97,4 +0,440,2.48,4 +0,400,3.35,3 +0,640,3.86,3 +0,440,3.13,4 +0,740,3.37,4 +1,680,3.27,2 +0,660,3.34,3 +1,740,4,3 +0,560,3.19,3 +0,380,2.94,3 +0,400,3.65,2 +0,600,2.82,4 +1,620,3.18,2 +0,560,3.32,4 +0,640,3.67,3 +1,680,3.85,3 +0,580,4,3 +0,600,3.59,2 +0,740,3.62,4 +0,620,3.3,1 +0,580,3.69,1 +0,800,3.73,1 +0,640,4,3 +0,300,2.92,4 +0,480,3.39,4 +0,580,4,2 +0,720,3.45,4 +0,720,4,3 +0,560,3.36,3 +1,800,4,3 +0,540,3.12,1 +1,620,4,1 +0,700,2.9,4 +0,620,3.07,2 +0,500,2.71,2 +0,380,2.91,4 +1,500,3.6,3 +0,520,2.98,2 +0,600,3.32,2 +0,600,3.48,2 +0,700,3.28,1 +1,660,4,2 +0,700,3.83,2 +1,720,3.64,1 +0,800,3.9,2 +0,580,2.93,2 +1,660,3.44,2 +0,660,3.33,2 +0,640,3.52,4 +0,480,3.57,2 +0,700,2.88,2 +0,400,3.31,3 +0,340,3.15,3 +0,580,3.57,3 +0,380,3.33,4 +0,540,3.94,3 +1,660,3.95,2 +1,740,2.97,2 +1,700,3.56,1 +0,480,3.13,2 +0,400,2.93,3 +0,480,3.45,2 +0,680,3.08,4 +0,420,3.41,4 +0,360,3,3 +0,600,3.22,1 +0,720,3.84,3 +0,620,3.99,3 +1,440,3.45,2 +0,700,3.72,2 +1,800,3.7,1 +0,340,2.92,3 +1,520,3.74,2 +1,480,2.67,2 +0,520,2.85,3 +0,500,2.98,3 +0,720,3.88,3 +0,540,3.38,4 +1,600,3.54,1 +0,740,3.74,4 +0,540,3.19,2 +0,460,3.15,4 +1,620,3.17,2 +0,640,2.79,2 +0,580,3.4,2 +0,500,3.08,3 +0,560,2.95,2 +0,500,3.57,3 +0,560,3.33,4 +0,700,4,3 +0,620,3.4,2 +1,600,3.58,1 +0,640,3.93,2 +1,700,3.52,4 +0,620,3.94,4 +0,580,3.4,3 +0,580,3.4,4 +0,380,3.43,3 +0,480,3.4,2 +0,560,2.71,3 +1,480,2.91,1 +0,740,3.31,1 +1,800,3.74,1 +0,400,3.38,2 +1,640,3.94,2 +0,580,3.46,3 +0,620,3.69,3 +1,580,2.86,4 +0,560,2.52,2 +1,480,3.58,1 +0,660,3.49,2 +0,700,3.82,3 +0,600,3.13,2 +0,640,3.5,2 +1,700,3.56,2 +0,520,2.73,2 +0,580,3.3,2 +0,700,4,1 +0,440,3.24,4 +0,720,3.77,3 +0,500,4,3 +0,600,3.62,3 +0,400,3.51,3 +0,540,2.81,3 +0,680,3.48,3 +1,800,3.43,2 +0,500,3.53,4 +1,620,3.37,2 +0,520,2.62,2 +1,620,3.23,3 +0,620,3.33,3 +0,300,3.01,3 +0,620,3.78,3 +0,500,3.88,4 +0,700,4,2 +1,540,3.84,2 +0,500,2.79,4 +0,800,3.6,2 +0,560,3.61,3 +0,580,2.88,2 +0,560,3.07,2 +0,500,3.35,2 +1,640,2.94,2 +0,800,3.54,3 +0,640,3.76,3 +0,380,3.59,4 +1,600,3.47,2 +0,560,3.59,2 +0,660,3.07,3 +1,400,3.23,4 +0,600,3.63,3 +0,580,3.77,4 +0,800,3.31,3 +1,580,3.2,2 +1,700,4,1 +0,420,3.92,4 +1,600,3.89,1 +1,780,3.8,3 +0,740,3.54,1 +1,640,3.63,1 +0,540,3.16,3 +0,580,3.5,2 +0,740,3.34,4 +0,580,3.02,2 +0,460,2.87,2 +0,640,3.38,3 +1,600,3.56,2 +1,660,2.91,3 +0,340,2.9,1 +1,460,3.64,1 +0,460,2.98,1 +1,560,3.59,2 +0,540,3.28,3 +0,680,3.99,3 +1,480,3.02,1 +0,800,3.47,3 +0,800,2.9,2 +1,720,3.5,3 +0,620,3.58,2 +0,540,3.02,4 +0,480,3.43,2 +1,720,3.42,2 +0,580,3.29,4 +0,600,3.28,3 +0,380,3.38,2 +0,420,2.67,3 +1,800,3.53,1 +0,620,3.05,2 +1,660,3.49,2 +0,480,4,2 +0,500,2.86,4 +0,700,3.45,3 +0,440,2.76,2 +1,520,3.81,1 +1,680,2.96,3 +0,620,3.22,2 +0,540,3.04,1 +0,800,3.91,3 +0,680,3.34,2 +0,440,3.17,2 +0,680,3.64,3 +0,640,3.73,3 +0,660,3.31,4 +0,620,3.21,4 +1,520,4,2 +1,540,3.55,4 +1,740,3.52,4 +0,640,3.35,3 +1,520,3.3,2 +1,620,3.95,3 +0,520,3.51,2 +0,640,3.81,2 +0,680,3.11,2 +0,440,3.15,2 +1,520,3.19,3 +1,620,3.95,3 +1,520,3.9,3 +0,380,3.34,3 +0,560,3.24,4 +1,600,3.64,3 +1,680,3.46,2 +0,500,2.81,3 +1,640,3.95,2 +0,540,3.33,3 +1,680,3.67,2 +0,660,3.32,1 +0,520,3.12,2 +1,600,2.98,2 +0,460,3.77,3 +1,580,3.58,1 +1,680,3,4 +1,660,3.14,2 +0,660,3.94,2 +0,360,3.27,3 +0,660,3.45,4 +0,520,3.1,4 +1,440,3.39,2 +0,600,3.31,4 +1,800,3.22,1 +1,660,3.7,4 +0,800,3.15,4 +0,420,2.26,4 +1,620,3.45,2 +0,800,2.78,2 +0,680,3.7,2 +0,800,3.97,1 +0,480,2.55,1 +0,520,3.25,3 +0,560,3.16,1 +0,460,3.07,2 +0,540,3.5,2 +0,720,3.4,3 +0,640,3.3,2 +1,660,3.6,3 +1,400,3.15,2 +1,680,3.98,2 +0,220,2.83,3 +0,580,3.46,4 +1,540,3.17,1 +0,580,3.51,2 +0,540,3.13,2 +0,440,2.98,3 +0,560,4,3 +0,660,3.67,2 +0,660,3.77,3 +1,520,3.65,4 +0,540,3.46,4 +1,300,2.84,2 +1,340,3,2 +1,780,3.63,4 +1,480,3.71,4 +0,540,3.28,1 +0,460,3.14,3 +0,460,3.58,2 +0,500,3.01,4 +0,420,2.69,2 +0,520,2.7,3 +0,680,3.9,1 +0,680,3.31,2 +1,560,3.48,2 +0,580,3.34,2 +0,500,2.93,4 +0,740,4,3 +0,660,3.59,3 +0,420,2.96,1 +0,560,3.43,3 +1,460,3.64,3 +1,620,3.71,1 +0,520,3.15,3 +0,620,3.09,4 +0,540,3.2,1 +1,660,3.47,3 +0,500,3.23,4 +1,560,2.65,3 +0,500,3.95,4 +0,580,3.06,2 +0,520,3.35,3 +0,500,3.03,3 +0,600,3.35,2 +0,580,3.8,2 +0,400,3.36,2 +0,620,2.85,2 +1,780,4,2 +0,620,3.43,3 +1,580,3.12,3 +0,700,3.52,2 +1,540,3.78,2 +1,760,2.81,1 +0,700,3.27,2 +0,720,3.31,1 +1,560,3.69,3 +0,720,3.94,3 +1,520,4,1 +1,540,3.49,1 +0,680,3.14,2 +0,460,3.44,2 +1,560,3.36,1 +0,480,2.78,3 +0,460,2.93,3 +0,620,3.63,3 +0,580,4,1 +0,800,3.89,2 +1,540,3.77,2 +1,680,3.76,3 +1,680,2.42,1 +1,620,3.37,1 +0,560,3.78,2 +0,560,3.49,4 +0,620,3.63,2 +1,800,4,2 +0,640,3.12,3 +0,540,2.7,2 +0,700,3.65,2 +1,540,3.49,2 +0,540,3.51,2 +0,660,4,1 +1,480,2.62,2 +0,420,3.02,1 +1,740,3.86,2 +0,580,3.36,2 +0,640,3.17,2 +0,640,3.51,2 +1,800,3.05,2 +1,660,3.88,2 +1,600,3.38,3 +1,620,3.75,2 +1,460,3.99,3 +0,620,4,2 +0,560,3.04,3 +0,460,2.63,2 +0,700,3.65,2 +0,600,3.89,3 diff --git a/python/Deep Learning/Implementing Gradient Descent/Backpropagation/data_prep.py b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/data_prep.py new file mode 100644 index 0000000..7de3f59 --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Backpropagation/data_prep.py @@ -0,0 +1,22 @@ +import numpy as np +import pandas as pd + +admissions = pd.read_csv('binary.csv') + +# Make dummy variables for rank +data = pd.concat([admissions, pd.get_dummies(admissions['rank'], prefix='rank')], axis=1) +data = data.drop('rank', axis=1) + +# Standarize features +for field in ['gre', 'gpa']: + mean, std = data[field].mean(), data[field].std() + data.loc[:,field] = (data[field]-mean)/std + +# Split off random 10% of the data for testing +np.random.seed(21) +sample = np.random.choice(data.index, size=int(len(data)*0.9), replace=False) +data, test_data = data.ix[sample], data.drop(sample) + +# Split into features and targets +features, targets = data.drop('admit', axis=1), data['admit'] +features_test, targets_test = test_data.drop('admit', axis=1), test_data['admit'] diff --git a/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/data_prep.py b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/data_prep.py new file mode 100644 index 0000000..6aab3ff --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/data_prep.py @@ -0,0 +1,24 @@ +import numpy as np +import pandas as pd + +admissions = pd.read_csv('binary.csv') + +# Make dummy variables for rank +data = pd.concat([admissions, pd.get_dummies( + admissions['rank'], prefix='rank')], axis=1) +data = data.drop('rank', axis=1) + +# Standarize features +for field in ['gre', 'gpa']: + mean, std = data[field].mean(), data[field].std() + data.loc[:, field] = (data[field] - mean) / std + +# Split off random 10% of the data for testing +np.random.seed(42) +sample = np.random.choice(data.index, size=int(len(data) * 0.9), replace=False) +data, test_data = data.ix[sample], data.drop(sample) + +# Split into features and targets +features, targets = data.drop('admit', axis=1), data['admit'] +features_test, targets_test = test_data.drop( + 'admit', axis=1), test_data['admit'] diff --git a/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient.py b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient.py new file mode 100644 index 0000000..a9ffbb0 --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient.py @@ -0,0 +1,56 @@ +import numpy as np + + +def sigmoid(x): + """ + Calculate sigmoid + """ + return 1 / (1 + np.exp(-x)) + + +def sigmoid_prime(x): + """ + # Derivative of the sigmoid function + """ + return sigmoid(x) * (1 - sigmoid(x)) + + +learnrate = 0.5 +x = np.array([1, 2, 3, 4]) +y = np.array(0.5) + +# Initial weights +w = np.array([0.5, -0.5, 0.3, 0.1]) + +# Calculate one gradient descent step for each weight +# Note: Some steps have been consolidated, so there are +# fewer variable names than in the above sample code + +# TODO: Calculate the node's linear combination of inputs and weights +h = np.dot(x, w) + +# TODO: Calculate output of neural network (y hat) +nn_output = sigmoid(h) + +# TODO: Calculate error of neural network (y - y hat) +error = y - nn_output + +# TODO: Calculate the error term +# Remember, this requires the output gradient, which we haven't +# specifically added a variable for. +error_term = error * sigmoid_prime(h) +# Note: The sigmoid_prime function calculates sigmoid(h) twice, +# but you've already calculated it once. You can make this +# code more efficient by calculating the derivative directly +# rather than calling sigmoid_prime, like this: +# error_term = error * nn_output * (1 - nn_output) + +# TODO: Calculate change in weights +del_w = learnrate * error_term * x + +print('Neural Network output:') +print(nn_output) +print('Amount of Error:') +print(error) +print('Change in Weights:') +print(del_w) diff --git a/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient_2.py b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient_2.py new file mode 100644 index 0000000..5246629 --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/gradient_2.py @@ -0,0 +1,71 @@ +import numpy as np +from data_prep import features, targets, features_test, targets_test + + +def sigmoid(x): + """ + Calculate sigmoid + """ + return 1 / (1 + np.exp(-x)) + +# TODO: We haven't provided the sigmoid_prime function like we did in +# the previous lesson to encourage you to come up with a more +# efficient solution. If you need a hint, check out the comments +# in solution.py from the previous lecture. + + +# Use to same seed to make debugging easier +np.random.seed(42) + +n_records, n_features = features.shape +last_loss = None + +# Initialize weights +weights = np.random.normal(scale=1 / n_features**.5, size=n_features) + +# Neural Network hyperparameters +epochs = 1000 +learnrate = 0.5 + +for e in range(epochs): + del_w = np.zeros(weights.shape) + for x, y in zip(features.values, targets): + # Loop through all records, x is the input, y is the target + + # Note: We haven't included the h variable from the previous + # lesson. You can add it if you want, or you can calculate + # the h together with the output + + # TODO: Calculate the output (y hat) + output = sigmoid(np.dot(x, weights)) + + # TODO: Calculate the error + error = y - output + + # TODO: Calculate the error term + error_term = error * output * (1 - output) + + # TODO: Calculate the change in weights for this sample + # and add it to the total weight change + del_w += error_term * x + + # TODO: Update weights using the learning rate and the average change in + # weights + weights += learnrate * del_w / n_records + + # Printing out the mean square error on the training set + if e % (epochs / 10) == 0: + out = sigmoid(np.dot(features, weights)) + loss = np.mean((out - targets) ** 2) + if last_loss and last_loss < loss: + print("Train loss: ", loss, " WARNING - Loss Increasing") + else: + print("Train loss: ", loss) + last_loss = loss + + +# Calculate accuracy on test data +tes_out = sigmoid(np.dot(features_test, weights)) +predictions = tes_out > 0.5 +accuracy = np.mean(predictions == targets_test) +print("Prediction accuracy: {:.3f}".format(accuracy)) diff --git a/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/multilayer.py b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/multilayer.py new file mode 100644 index 0000000..48a7a0e --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Multilayer Perceptron/multilayer.py @@ -0,0 +1,38 @@ +import numpy as np + + +def sigmoid(x): + """ + Calculate sigmoid + """ + return 1 / (1 + np.exp(-x)) + + +# Network size +N_input = 4 +N_hidden = 3 +N_output = 2 + +np.random.seed(42) +# Make some fake data +X = np.random.randn(4) + +weights_input_to_hidden = np.random.normal( + 0, scale=0.1, size=(N_input, N_hidden)) +weights_hidden_to_output = np.random.normal( + 0, scale=0.1, size=(N_hidden, N_output)) + + +# TODO: Make a forward pass through the network + +hidden_layer_in = np.dot(X, weights_input_to_hidden) +hidden_layer_out = sigmoid(hidden_layer_in) + +print('Hidden-layer Output:') +print(hidden_layer_out) + +output_layer_in = np.dot(hidden_layer_out, weights_hidden_to_output) +output_layer_out = sigmoid(output_layer_in) + +print('Output-layer Output:') +print(output_layer_out) diff --git a/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/binary.csv b/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/binary.csv new file mode 100644 index 0000000..5f2cf4e --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/binary.csv @@ -0,0 +1,401 @@ +admit,gre,gpa,rank +0,380,3.61,3 +1,660,3.67,3 +1,800,4,1 +1,640,3.19,4 +0,520,2.93,4 +1,760,3,2 +1,560,2.98,1 +0,400,3.08,2 +1,540,3.39,3 +0,700,3.92,2 +0,800,4,4 +0,440,3.22,1 +1,760,4,1 +0,700,3.08,2 +1,700,4,1 +0,480,3.44,3 +0,780,3.87,4 +0,360,2.56,3 +0,800,3.75,2 +1,540,3.81,1 +0,500,3.17,3 +1,660,3.63,2 +0,600,2.82,4 +0,680,3.19,4 +1,760,3.35,2 +1,800,3.66,1 +1,620,3.61,1 +1,520,3.74,4 +1,780,3.22,2 +0,520,3.29,1 +0,540,3.78,4 +0,760,3.35,3 +0,600,3.4,3 +1,800,4,3 +0,360,3.14,1 +0,400,3.05,2 +0,580,3.25,1 +0,520,2.9,3 +1,500,3.13,2 +1,520,2.68,3 +0,560,2.42,2 +1,580,3.32,2 +1,600,3.15,2 +0,500,3.31,3 +0,700,2.94,2 +1,460,3.45,3 +1,580,3.46,2 +0,500,2.97,4 +0,440,2.48,4 +0,400,3.35,3 +0,640,3.86,3 +0,440,3.13,4 +0,740,3.37,4 +1,680,3.27,2 +0,660,3.34,3 +1,740,4,3 +0,560,3.19,3 +0,380,2.94,3 +0,400,3.65,2 +0,600,2.82,4 +1,620,3.18,2 +0,560,3.32,4 +0,640,3.67,3 +1,680,3.85,3 +0,580,4,3 +0,600,3.59,2 +0,740,3.62,4 +0,620,3.3,1 +0,580,3.69,1 +0,800,3.73,1 +0,640,4,3 +0,300,2.92,4 +0,480,3.39,4 +0,580,4,2 +0,720,3.45,4 +0,720,4,3 +0,560,3.36,3 +1,800,4,3 +0,540,3.12,1 +1,620,4,1 +0,700,2.9,4 +0,620,3.07,2 +0,500,2.71,2 +0,380,2.91,4 +1,500,3.6,3 +0,520,2.98,2 +0,600,3.32,2 +0,600,3.48,2 +0,700,3.28,1 +1,660,4,2 +0,700,3.83,2 +1,720,3.64,1 +0,800,3.9,2 +0,580,2.93,2 +1,660,3.44,2 +0,660,3.33,2 +0,640,3.52,4 +0,480,3.57,2 +0,700,2.88,2 +0,400,3.31,3 +0,340,3.15,3 +0,580,3.57,3 +0,380,3.33,4 +0,540,3.94,3 +1,660,3.95,2 +1,740,2.97,2 +1,700,3.56,1 +0,480,3.13,2 +0,400,2.93,3 +0,480,3.45,2 +0,680,3.08,4 +0,420,3.41,4 +0,360,3,3 +0,600,3.22,1 +0,720,3.84,3 +0,620,3.99,3 +1,440,3.45,2 +0,700,3.72,2 +1,800,3.7,1 +0,340,2.92,3 +1,520,3.74,2 +1,480,2.67,2 +0,520,2.85,3 +0,500,2.98,3 +0,720,3.88,3 +0,540,3.38,4 +1,600,3.54,1 +0,740,3.74,4 +0,540,3.19,2 +0,460,3.15,4 +1,620,3.17,2 +0,640,2.79,2 +0,580,3.4,2 +0,500,3.08,3 +0,560,2.95,2 +0,500,3.57,3 +0,560,3.33,4 +0,700,4,3 +0,620,3.4,2 +1,600,3.58,1 +0,640,3.93,2 +1,700,3.52,4 +0,620,3.94,4 +0,580,3.4,3 +0,580,3.4,4 +0,380,3.43,3 +0,480,3.4,2 +0,560,2.71,3 +1,480,2.91,1 +0,740,3.31,1 +1,800,3.74,1 +0,400,3.38,2 +1,640,3.94,2 +0,580,3.46,3 +0,620,3.69,3 +1,580,2.86,4 +0,560,2.52,2 +1,480,3.58,1 +0,660,3.49,2 +0,700,3.82,3 +0,600,3.13,2 +0,640,3.5,2 +1,700,3.56,2 +0,520,2.73,2 +0,580,3.3,2 +0,700,4,1 +0,440,3.24,4 +0,720,3.77,3 +0,500,4,3 +0,600,3.62,3 +0,400,3.51,3 +0,540,2.81,3 +0,680,3.48,3 +1,800,3.43,2 +0,500,3.53,4 +1,620,3.37,2 +0,520,2.62,2 +1,620,3.23,3 +0,620,3.33,3 +0,300,3.01,3 +0,620,3.78,3 +0,500,3.88,4 +0,700,4,2 +1,540,3.84,2 +0,500,2.79,4 +0,800,3.6,2 +0,560,3.61,3 +0,580,2.88,2 +0,560,3.07,2 +0,500,3.35,2 +1,640,2.94,2 +0,800,3.54,3 +0,640,3.76,3 +0,380,3.59,4 +1,600,3.47,2 +0,560,3.59,2 +0,660,3.07,3 +1,400,3.23,4 +0,600,3.63,3 +0,580,3.77,4 +0,800,3.31,3 +1,580,3.2,2 +1,700,4,1 +0,420,3.92,4 +1,600,3.89,1 +1,780,3.8,3 +0,740,3.54,1 +1,640,3.63,1 +0,540,3.16,3 +0,580,3.5,2 +0,740,3.34,4 +0,580,3.02,2 +0,460,2.87,2 +0,640,3.38,3 +1,600,3.56,2 +1,660,2.91,3 +0,340,2.9,1 +1,460,3.64,1 +0,460,2.98,1 +1,560,3.59,2 +0,540,3.28,3 +0,680,3.99,3 +1,480,3.02,1 +0,800,3.47,3 +0,800,2.9,2 +1,720,3.5,3 +0,620,3.58,2 +0,540,3.02,4 +0,480,3.43,2 +1,720,3.42,2 +0,580,3.29,4 +0,600,3.28,3 +0,380,3.38,2 +0,420,2.67,3 +1,800,3.53,1 +0,620,3.05,2 +1,660,3.49,2 +0,480,4,2 +0,500,2.86,4 +0,700,3.45,3 +0,440,2.76,2 +1,520,3.81,1 +1,680,2.96,3 +0,620,3.22,2 +0,540,3.04,1 +0,800,3.91,3 +0,680,3.34,2 +0,440,3.17,2 +0,680,3.64,3 +0,640,3.73,3 +0,660,3.31,4 +0,620,3.21,4 +1,520,4,2 +1,540,3.55,4 +1,740,3.52,4 +0,640,3.35,3 +1,520,3.3,2 +1,620,3.95,3 +0,520,3.51,2 +0,640,3.81,2 +0,680,3.11,2 +0,440,3.15,2 +1,520,3.19,3 +1,620,3.95,3 +1,520,3.9,3 +0,380,3.34,3 +0,560,3.24,4 +1,600,3.64,3 +1,680,3.46,2 +0,500,2.81,3 +1,640,3.95,2 +0,540,3.33,3 +1,680,3.67,2 +0,660,3.32,1 +0,520,3.12,2 +1,600,2.98,2 +0,460,3.77,3 +1,580,3.58,1 +1,680,3,4 +1,660,3.14,2 +0,660,3.94,2 +0,360,3.27,3 +0,660,3.45,4 +0,520,3.1,4 +1,440,3.39,2 +0,600,3.31,4 +1,800,3.22,1 +1,660,3.7,4 +0,800,3.15,4 +0,420,2.26,4 +1,620,3.45,2 +0,800,2.78,2 +0,680,3.7,2 +0,800,3.97,1 +0,480,2.55,1 +0,520,3.25,3 +0,560,3.16,1 +0,460,3.07,2 +0,540,3.5,2 +0,720,3.4,3 +0,640,3.3,2 +1,660,3.6,3 +1,400,3.15,2 +1,680,3.98,2 +0,220,2.83,3 +0,580,3.46,4 +1,540,3.17,1 +0,580,3.51,2 +0,540,3.13,2 +0,440,2.98,3 +0,560,4,3 +0,660,3.67,2 +0,660,3.77,3 +1,520,3.65,4 +0,540,3.46,4 +1,300,2.84,2 +1,340,3,2 +1,780,3.63,4 +1,480,3.71,4 +0,540,3.28,1 +0,460,3.14,3 +0,460,3.58,2 +0,500,3.01,4 +0,420,2.69,2 +0,520,2.7,3 +0,680,3.9,1 +0,680,3.31,2 +1,560,3.48,2 +0,580,3.34,2 +0,500,2.93,4 +0,740,4,3 +0,660,3.59,3 +0,420,2.96,1 +0,560,3.43,3 +1,460,3.64,3 +1,620,3.71,1 +0,520,3.15,3 +0,620,3.09,4 +0,540,3.2,1 +1,660,3.47,3 +0,500,3.23,4 +1,560,2.65,3 +0,500,3.95,4 +0,580,3.06,2 +0,520,3.35,3 +0,500,3.03,3 +0,600,3.35,2 +0,580,3.8,2 +0,400,3.36,2 +0,620,2.85,2 +1,780,4,2 +0,620,3.43,3 +1,580,3.12,3 +0,700,3.52,2 +1,540,3.78,2 +1,760,2.81,1 +0,700,3.27,2 +0,720,3.31,1 +1,560,3.69,3 +0,720,3.94,3 +1,520,4,1 +1,540,3.49,1 +0,680,3.14,2 +0,460,3.44,2 +1,560,3.36,1 +0,480,2.78,3 +0,460,2.93,3 +0,620,3.63,3 +0,580,4,1 +0,800,3.89,2 +1,540,3.77,2 +1,680,3.76,3 +1,680,2.42,1 +1,620,3.37,1 +0,560,3.78,2 +0,560,3.49,4 +0,620,3.63,2 +1,800,4,2 +0,640,3.12,3 +0,540,2.7,2 +0,700,3.65,2 +1,540,3.49,2 +0,540,3.51,2 +0,660,4,1 +1,480,2.62,2 +0,420,3.02,1 +1,740,3.86,2 +0,580,3.36,2 +0,640,3.17,2 +0,640,3.51,2 +1,800,3.05,2 +1,660,3.88,2 +1,600,3.38,3 +1,620,3.75,2 +1,460,3.99,3 +0,620,4,2 +0,560,3.04,3 +0,460,2.63,2 +0,700,3.65,2 +0,600,3.89,3 diff --git a/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/data_prep.py b/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/data_prep.py new file mode 100644 index 0000000..6aab3ff --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/data_prep.py @@ -0,0 +1,24 @@ +import numpy as np +import pandas as pd + +admissions = pd.read_csv('binary.csv') + +# Make dummy variables for rank +data = pd.concat([admissions, pd.get_dummies( + admissions['rank'], prefix='rank')], axis=1) +data = data.drop('rank', axis=1) + +# Standarize features +for field in ['gre', 'gpa']: + mean, std = data[field].mean(), data[field].std() + data.loc[:, field] = (data[field] - mean) / std + +# Split off random 10% of the data for testing +np.random.seed(42) +sample = np.random.choice(data.index, size=int(len(data) * 0.9), replace=False) +data, test_data = data.ix[sample], data.drop(sample) + +# Split into features and targets +features, targets = data.drop('admit', axis=1), data['admit'] +features_test, targets_test = test_data.drop( + 'admit', axis=1), test_data['admit'] diff --git a/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/gradient_2.py b/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/gradient_2.py new file mode 100644 index 0000000..5246629 --- /dev/null +++ b/python/Deep Learning/Implementing Gradient Descent/Single Perceptron/gradient_2.py @@ -0,0 +1,71 @@ +import numpy as np +from data_prep import features, targets, features_test, targets_test + + +def sigmoid(x): + """ + Calculate sigmoid + """ + return 1 / (1 + np.exp(-x)) + +# TODO: We haven't provided the sigmoid_prime function like we did in +# the previous lesson to encourage you to come up with a more +# efficient solution. If you need a hint, check out the comments +# in solution.py from the previous lecture. + + +# Use to same seed to make debugging easier +np.random.seed(42) + +n_records, n_features = features.shape +last_loss = None + +# Initialize weights +weights = np.random.normal(scale=1 / n_features**.5, size=n_features) + +# Neural Network hyperparameters +epochs = 1000 +learnrate = 0.5 + +for e in range(epochs): + del_w = np.zeros(weights.shape) + for x, y in zip(features.values, targets): + # Loop through all records, x is the input, y is the target + + # Note: We haven't included the h variable from the previous + # lesson. You can add it if you want, or you can calculate + # the h together with the output + + # TODO: Calculate the output (y hat) + output = sigmoid(np.dot(x, weights)) + + # TODO: Calculate the error + error = y - output + + # TODO: Calculate the error term + error_term = error * output * (1 - output) + + # TODO: Calculate the change in weights for this sample + # and add it to the total weight change + del_w += error_term * x + + # TODO: Update weights using the learning rate and the average change in + # weights + weights += learnrate * del_w / n_records + + # Printing out the mean square error on the training set + if e % (epochs / 10) == 0: + out = sigmoid(np.dot(features, weights)) + loss = np.mean((out - targets) ** 2) + if last_loss and last_loss < loss: + print("Train loss: ", loss, " WARNING - Loss Increasing") + else: + print("Train loss: ", loss) + last_loss = loss + + +# Calculate accuracy on test data +tes_out = sigmoid(np.dot(features_test, weights)) +predictions = tes_out > 0.5 +accuracy = np.mean(predictions == targets_test) +print("Prediction accuracy: {:.3f}".format(accuracy)) diff --git a/python/Deep Learning/Implementing Gradient Descent/__pycache__/data_prep.cpython-37.pyc b/python/Deep Learning/Implementing Gradient Descent/__pycache__/data_prep.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ab6efe99182a2fea2da8de5748738ea32cb1acb0 GIT binary patch literal 823 zcmX|8&2G~`5Z<*N$8nPK7edn_WFam=qB(G_0HFv}2`VIn5Gz&H;+Z5H;n z*b^_19C;UbfxU97#1n90oTiNAZ|Be7H#_^h-HsXAhX;>Oc6`QuSHYEq~R|_WAfdLq6SEU4v-N`Np4Kr>+}# z6K`Skl|!S$2xDlT`=UdY+t}$5&#Hl)H7AWobT?DAE~ml>TD?m9j@ZR6O?#E^Zu32{ z2mCGTw=b&Q9G}G>m6m3CklHUUFj9Z&a~Bo{M{03FyJ8dua-nRW7Bx0VP{@miR2cvI zS=pbbFZ!MftU5ziH(1PMilQbVQ)PeO)$}|omd?|K2n(q}TIUrYB7+i6Nm9gBr?o2A z)`e-VQ(21U7|TE}XA)No}ZIXk}K+(0OVh8YNoNAZizlP?mE;QuR_AlD4jWl{&53Tx*-(L_e6h zN4MX=-|vG-p5Xw>Jew(Pb3I7&xv^*m??9%iTqdt&I#C*vS165EdYn8;j&x~qn5QLO zIysg)2c*Sf^2X$2BeOxVEGNV?L@fUQHaMzA&rp{Y$4etYk)I6FrgRRfi=5J<6$iBJ k=rAMt89u3=&)y}7c;v-?%)5MtaF5^jTRh=if0qaRAI+}v#sB~S literal 0 HcmV?d00001 diff --git a/python/Supervised Learning/Project/.ipynb_checkpoints/finding_donors-checkpoint.ipynb b/python/Supervised Learning/Project/.ipynb_checkpoints/finding_donors-checkpoint.ipynb index 3c24309..4f0c914 100644 --- a/python/Supervised Learning/Project/.ipynb_checkpoints/finding_donors-checkpoint.ipynb +++ b/python/Supervised Learning/Project/.ipynb_checkpoints/finding_donors-checkpoint.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -196,7 +196,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -217,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -293,7 +293,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -303,16 +303,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, @@ -342,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 31, "metadata": {}, "outputs": [ { @@ -351,7 +351,7 @@ "(0, 1500)" ] }, - "execution_count": 8, + "execution_count": 31, "metadata": {}, "output_type": "execute_result" }, @@ -376,7 +376,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 32, "metadata": {}, "outputs": [ { @@ -385,7 +385,7 @@ "(0, 1000)" ] }, - "execution_count": 9, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, @@ -410,7 +410,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -446,7 +446,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 34, "metadata": {}, "outputs": [ { @@ -474,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 35, "metadata": {}, "outputs": [ { @@ -483,7 +483,7 @@ "(0, 1500)" ] }, - "execution_count": 12, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" }, @@ -508,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 36, "metadata": {}, "outputs": [ { @@ -517,7 +517,7 @@ "(0, 1500)" ] }, - "execution_count": 13, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, @@ -552,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 37, "metadata": {}, "outputs": [ { @@ -740,7 +740,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -781,83 +781,83 @@ " \n", " \n", " \n", - " 38017\n", - " 0.260274\n", + " 14204\n", + " 0.027397\n", " Private\n", " HS-grad\n", " 0.533333\n", " Never-married\n", - " Adm-clerical\n", - " Unmarried\n", - " White\n", + " Other-service\n", + " Own-child\n", + " Black\n", " Female\n", - " 0.000000\n", + " 0.0\n", " 0.0\n", " 0.397959\n", " United-States\n", " \n", " \n", - " 13752\n", - " 0.219178\n", + " 4740\n", + " 0.068493\n", " Private\n", - " HS-grad\n", - " 0.533333\n", - " Married-civ-spouse\n", - " Transport-moving\n", - " Husband\n", + " Assoc-voc\n", + " 0.666667\n", + " Never-married\n", + " Prof-specialty\n", + " Not-in-family\n", " White\n", " Male\n", - " 0.000000\n", " 0.0\n", - " 0.397959\n", + " 0.0\n", + " 0.295918\n", " United-States\n", " \n", " \n", - " 31365\n", - " 0.054795\n", + " 19821\n", + " 0.027397\n", " Private\n", - " HS-grad\n", - " 0.533333\n", + " Some-college\n", + " 0.600000\n", " Never-married\n", - " Handlers-cleaners\n", - " Not-in-family\n", - " Asian-Pac-Islander\n", - " Female\n", - " 0.000000\n", + " Other-service\n", + " Own-child\n", + " White\n", + " Male\n", " 0.0\n", - " 0.336735\n", - " South\n", + " 0.0\n", + " 0.244898\n", + " United-States\n", " \n", " \n", - " 8526\n", + " 15539\n", " 0.219178\n", - " Private\n", - " 7th-8th\n", - " 0.200000\n", + " Self-emp-not-inc\n", + " 11th\n", + " 0.400000\n", " Married-civ-spouse\n", " Craft-repair\n", " Husband\n", " White\n", " Male\n", - " 0.000000\n", " 0.0\n", - " 0.397959\n", + " 0.0\n", + " 0.500000\n", " United-States\n", " \n", " \n", - " 32263\n", - " 0.534247\n", + " 416\n", + " 0.041096\n", " Private\n", - " Doctorate\n", - " 1.000000\n", + " HS-grad\n", + " 0.533333\n", " Married-civ-spouse\n", - " Prof-specialty\n", + " Machine-op-inspct\n", " Husband\n", " White\n", " Male\n", - " 0.777174\n", " 0.0\n", - " 0.653061\n", + " 0.0\n", + " 0.397959\n", " United-States\n", " \n", " \n", @@ -865,29 +865,29 @@ "" ], "text/plain": [ - " age workclass education_level education-num marital-status \\\n", - "38017 0.260274 Private HS-grad 0.533333 Never-married \n", - "13752 0.219178 Private HS-grad 0.533333 Married-civ-spouse \n", - "31365 0.054795 Private HS-grad 0.533333 Never-married \n", - "8526 0.219178 Private 7th-8th 0.200000 Married-civ-spouse \n", - "32263 0.534247 Private Doctorate 1.000000 Married-civ-spouse \n", + " age workclass education_level education-num \\\n", + "14204 0.027397 Private HS-grad 0.533333 \n", + "4740 0.068493 Private Assoc-voc 0.666667 \n", + "19821 0.027397 Private Some-college 0.600000 \n", + "15539 0.219178 Self-emp-not-inc 11th 0.400000 \n", + "416 0.041096 Private HS-grad 0.533333 \n", "\n", - " occupation relationship race sex \\\n", - "38017 Adm-clerical Unmarried White Female \n", - "13752 Transport-moving Husband White Male \n", - "31365 Handlers-cleaners Not-in-family Asian-Pac-Islander Female \n", - "8526 Craft-repair Husband White Male \n", - "32263 Prof-specialty Husband White Male \n", + " marital-status occupation relationship race \\\n", + "14204 Never-married Other-service Own-child Black \n", + "4740 Never-married Prof-specialty Not-in-family White \n", + "19821 Never-married Other-service Own-child White \n", + "15539 Married-civ-spouse Craft-repair Husband White \n", + "416 Married-civ-spouse Machine-op-inspct Husband White \n", "\n", - " capital-gain capital-loss hours-per-week native-country \n", - "38017 0.000000 0.0 0.397959 United-States \n", - "13752 0.000000 0.0 0.397959 United-States \n", - "31365 0.000000 0.0 0.336735 South \n", - "8526 0.000000 0.0 0.397959 United-States \n", - "32263 0.777174 0.0 0.653061 United-States " + " sex capital-gain capital-loss hours-per-week native-country \n", + "14204 Female 0.0 0.0 0.397959 United-States \n", + "4740 Male 0.0 0.0 0.295918 United-States \n", + "19821 Male 0.0 0.0 0.244898 United-States \n", + "15539 Male 0.0 0.0 0.500000 United-States \n", + "416 Male 0.0 0.0 0.397959 United-States " ] }, - "execution_count": 15, + "execution_count": 38, "metadata": {}, "output_type": "execute_result" } @@ -899,7 +899,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 39, "metadata": { "scrolled": true }, @@ -914,11 +914,11 @@ "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n", "\n", "The income col now looks like:\n", - "19232 0\n", - "45209 0\n", - "26283 1\n", - "41688 0\n", - "9039 0\n", + "16481 0\n", + "37818 0\n", + "20804 0\n", + "3242 1\n", + "23475 0\n", "Name: income, dtype: object\n" ] } @@ -949,7 +949,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 40, "metadata": {}, "outputs": [ { @@ -961,7 +961,7 @@ "Name: income, dtype: int32" ] }, - "execution_count": 17, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -993,7 +993,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 41, "metadata": {}, "outputs": [ { @@ -1042,10 +1042,58 @@ " \n", " \n", " \n", - " 13249\n", - " 0.109589\n", + " 16702\n", + " 0.383562\n", " 0.866667\n", - " 0.000000\n", + " 0.0\n", + " 0.0\n", + " 0.346939\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " 0\n", + " ...\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 8879\n", + " 0.520548\n", + " 0.333333\n", + " 0.0\n", + " 0.0\n", + " 0.285714\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " ...\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 14928\n", + " 0.150685\n", + " 0.600000\n", + " 0.0\n", " 0.0\n", " 0.397959\n", " 0\n", @@ -1066,34 +1114,10 @@ " 0\n", " \n", " \n", - " 22542\n", - " 0.410959\n", + " 28087\n", + " 0.273973\n", " 0.533333\n", - " 0.787051\n", " 0.0\n", - " 0.438776\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 1236\n", - " 0.246575\n", - " 0.800000\n", - " 0.000000\n", " 0.0\n", " 0.397959\n", " 0\n", @@ -1114,34 +1138,10 @@ " 0\n", " \n", " \n", - " 23702\n", - " 0.369863\n", - " 0.800000\n", - " 0.000000\n", - " 0.0\n", - " 0.500000\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 8339\n", - " 0.356164\n", + " 33970\n", + " 0.232877\n", " 0.533333\n", - " 0.000000\n", + " 0.0\n", " 0.0\n", " 0.397959\n", " 0\n", @@ -1168,65 +1168,65 @@ ], "text/plain": [ " age education-num capital-gain capital-loss hours-per-week \\\n", - "13249 0.109589 0.866667 0.000000 0.0 0.397959 \n", - "22542 0.410959 0.533333 0.787051 0.0 0.438776 \n", - "1236 0.246575 0.800000 0.000000 0.0 0.397959 \n", - "23702 0.369863 0.800000 0.000000 0.0 0.500000 \n", - "8339 0.356164 0.533333 0.000000 0.0 0.397959 \n", + "16702 0.383562 0.866667 0.0 0.0 0.346939 \n", + "8879 0.520548 0.333333 0.0 0.0 0.285714 \n", + "14928 0.150685 0.600000 0.0 0.0 0.397959 \n", + "28087 0.273973 0.533333 0.0 0.0 0.397959 \n", + "33970 0.232877 0.533333 0.0 0.0 0.397959 \n", "\n", " workclass_ Federal-gov workclass_ Local-gov workclass_ Private \\\n", - "13249 0 0 1 \n", - "22542 0 0 1 \n", - "1236 0 0 1 \n", - "23702 0 0 1 \n", - "8339 0 0 1 \n", + "16702 0 1 0 \n", + "8879 0 0 1 \n", + "14928 0 0 1 \n", + "28087 0 0 1 \n", + "33970 0 0 1 \n", "\n", " workclass_ Self-emp-inc workclass_ Self-emp-not-inc ... \\\n", - "13249 0 0 ... \n", - "22542 0 0 ... \n", - "1236 0 0 ... \n", - "23702 0 0 ... \n", - "8339 0 0 ... \n", + "16702 0 0 ... \n", + "8879 0 0 ... \n", + "14928 0 0 ... \n", + "28087 0 0 ... \n", + "33970 0 0 ... \n", "\n", " native-country_ Portugal native-country_ Puerto-Rico \\\n", - "13249 0 0 \n", - "22542 0 0 \n", - "1236 0 0 \n", - "23702 0 0 \n", - "8339 0 0 \n", + "16702 0 0 \n", + "8879 0 0 \n", + "14928 0 0 \n", + "28087 0 0 \n", + "33970 0 0 \n", "\n", " native-country_ Scotland native-country_ South \\\n", - "13249 0 0 \n", - "22542 0 0 \n", - "1236 0 0 \n", - "23702 0 0 \n", - "8339 0 0 \n", + "16702 0 0 \n", + "8879 0 0 \n", + "14928 0 0 \n", + "28087 0 0 \n", + "33970 0 0 \n", "\n", " native-country_ Taiwan native-country_ Thailand \\\n", - "13249 0 0 \n", - "22542 0 0 \n", - "1236 0 0 \n", - "23702 0 0 \n", - "8339 0 0 \n", + "16702 0 0 \n", + "8879 0 0 \n", + "14928 0 0 \n", + "28087 0 0 \n", + "33970 0 0 \n", "\n", " native-country_ Trinadad&Tobago native-country_ United-States \\\n", - "13249 0 1 \n", - "22542 0 1 \n", - "1236 0 1 \n", - "23702 0 1 \n", - "8339 0 1 \n", + "16702 0 1 \n", + "8879 0 1 \n", + "14928 0 1 \n", + "28087 0 1 \n", + "33970 0 1 \n", "\n", " native-country_ Vietnam native-country_ Yugoslavia \n", - "13249 0 0 \n", - "22542 0 0 \n", - "1236 0 0 \n", - "23702 0 0 \n", - "8339 0 0 \n", + "16702 0 0 \n", + "8879 0 0 \n", + "14928 0 0 \n", + "28087 0 0 \n", + "33970 0 0 \n", "\n", "[5 rows x 103 columns]" ] }, - "execution_count": 18, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } @@ -1237,7 +1237,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 42, "metadata": {}, "outputs": [ { @@ -1321,7 +1321,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -1333,7 +1333,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 44, "metadata": {}, "outputs": [ { @@ -1468,7 +1468,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 45, "metadata": {}, "outputs": [ { @@ -1578,7 +1578,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -1652,7 +1652,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 47, "metadata": {}, "outputs": [ { @@ -1666,7 +1666,7 @@ "Name: income, dtype: int32" ] }, - "execution_count": 24, + "execution_count": 47, "metadata": {}, "output_type": "execute_result" } @@ -1677,7 +1677,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -1708,7 +1708,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1824,7 +1824,65 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 51, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,\n", + " decision_function_shape='ovr', degree=3, gamma=0.1, kernel='rbf',\n", + " max_iter=-1, probability=False, random_state=None, shrinking=True,\n", + " tol=0.001, verbose=False)" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.model_selection import GridSearchCV\n", + "from sklearn.metrics import make_scorer\n", + "\n", + "clf = SVC(C=0.01, gamma=0.1, kernel='rbf')\n", + "\n", + "clf.fit(X_train, y_train)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "predictions_test = clf.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8245439469320066" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "accuracy_score(y_test, predictions_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, "metadata": {}, "outputs": [ { @@ -1834,7 +1892,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mgrid_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Get the estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mgrid_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Get the estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 665\u001b[0m in product(candidate_params,\n\u001b[0;32m--> 666\u001b[0;31m cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m 667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", @@ -1865,7 +1923,7 @@ "scorer = make_scorer(fbeta_score, beta=0.5)\n", "\n", "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n", - "grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer, n_jobs=-1)\n", + "grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer)\n", "\n", "# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\n", "grid_fit = grid_obj.fit(X_train, y_train)\n", diff --git a/python/Supervised Learning/Project/finding_donors.ipynb b/python/Supervised Learning/Project/finding_donors.ipynb index 8b4944e..99f779d 100644 --- a/python/Supervised Learning/Project/finding_donors.ipynb +++ b/python/Supervised Learning/Project/finding_donors.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -165,7 +165,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -196,8 +196,10 @@ }, { "cell_type": "code", - "execution_count": 27, - "metadata": {}, + "execution_count": 4, + "metadata": { + "scrolled": true + }, "outputs": [ { "name": "stdout", @@ -217,7 +219,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -293,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -303,16 +305,16 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 30, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, @@ -342,7 +344,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -351,7 +353,7 @@ "(0, 1500)" ] }, - "execution_count": 31, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, @@ -376,7 +378,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -385,7 +387,7 @@ "(0, 1000)" ] }, - "execution_count": 32, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, @@ -410,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -446,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -474,7 +476,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -483,7 +485,7 @@ "(0, 1500)" ] }, - "execution_count": 35, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, @@ -508,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -517,7 +519,7 @@ "(0, 1500)" ] }, - "execution_count": 36, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, @@ -552,7 +554,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -740,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -781,59 +783,43 @@ " \n", " \n", " \n", - " 14204\n", - " 0.027397\n", - " Private\n", - " HS-grad\n", - " 0.533333\n", - " Never-married\n", - " Other-service\n", - " Own-child\n", - " Black\n", - " Female\n", + " 31082\n", + " 0.260274\n", + " Local-gov\n", + " Some-college\n", + " 0.600000\n", + " Married-civ-spouse\n", + " Exec-managerial\n", + " Husband\n", + " White\n", + " Male\n", " 0.0\n", " 0.0\n", " 0.397959\n", " United-States\n", " \n", " \n", - " 4740\n", - " 0.068493\n", - " Private\n", - " Assoc-voc\n", - " 0.666667\n", - " Never-married\n", - " Prof-specialty\n", - " Not-in-family\n", + " 43423\n", + " 0.356164\n", + " Self-emp-not-inc\n", + " HS-grad\n", + " 0.533333\n", + " Married-civ-spouse\n", + " Adm-clerical\n", + " Wife\n", " White\n", - " Male\n", + " Female\n", " 0.0\n", " 0.0\n", - " 0.295918\n", + " 0.142857\n", " United-States\n", " \n", " \n", - " 19821\n", - " 0.027397\n", + " 29225\n", + " 0.383562\n", " Private\n", " Some-college\n", " 0.600000\n", - " Never-married\n", - " Other-service\n", - " Own-child\n", - " White\n", - " Male\n", - " 0.0\n", - " 0.0\n", - " 0.244898\n", - " United-States\n", - " \n", - " \n", - " 15539\n", - " 0.219178\n", - " Self-emp-not-inc\n", - " 11th\n", - " 0.400000\n", " Married-civ-spouse\n", " Craft-repair\n", " Husband\n", @@ -845,49 +831,65 @@ " United-States\n", " \n", " \n", - " 416\n", - " 0.041096\n", - " Private\n", - " HS-grad\n", - " 0.533333\n", + " 8419\n", + " 0.410959\n", + " Federal-gov\n", + " Some-college\n", + " 0.600000\n", " Married-civ-spouse\n", - " Machine-op-inspct\n", + " Adm-clerical\n", " Husband\n", - " White\n", + " Black\n", " Male\n", " 0.0\n", " 0.0\n", " 0.397959\n", " United-States\n", " \n", + " \n", + " 35362\n", + " 0.164384\n", + " Private\n", + " 11th\n", + " 0.400000\n", + " Never-married\n", + " Machine-op-inspct\n", + " Own-child\n", + " White\n", + " Male\n", + " 0.0\n", + " 0.0\n", + " 0.418367\n", + " United-States\n", + " \n", " \n", "\n", "" ], "text/plain": [ " age workclass education_level education-num \\\n", - "14204 0.027397 Private HS-grad 0.533333 \n", - "4740 0.068493 Private Assoc-voc 0.666667 \n", - "19821 0.027397 Private Some-college 0.600000 \n", - "15539 0.219178 Self-emp-not-inc 11th 0.400000 \n", - "416 0.041096 Private HS-grad 0.533333 \n", + "31082 0.260274 Local-gov Some-college 0.600000 \n", + "43423 0.356164 Self-emp-not-inc HS-grad 0.533333 \n", + "29225 0.383562 Private Some-college 0.600000 \n", + "8419 0.410959 Federal-gov Some-college 0.600000 \n", + "35362 0.164384 Private 11th 0.400000 \n", "\n", - " marital-status occupation relationship race \\\n", - "14204 Never-married Other-service Own-child Black \n", - "4740 Never-married Prof-specialty Not-in-family White \n", - "19821 Never-married Other-service Own-child White \n", - "15539 Married-civ-spouse Craft-repair Husband White \n", - "416 Married-civ-spouse Machine-op-inspct Husband White \n", + " marital-status occupation relationship race sex \\\n", + "31082 Married-civ-spouse Exec-managerial Husband White Male \n", + "43423 Married-civ-spouse Adm-clerical Wife White Female \n", + "29225 Married-civ-spouse Craft-repair Husband White Male \n", + "8419 Married-civ-spouse Adm-clerical Husband Black Male \n", + "35362 Never-married Machine-op-inspct Own-child White Male \n", "\n", - " sex capital-gain capital-loss hours-per-week native-country \n", - "14204 Female 0.0 0.0 0.397959 United-States \n", - "4740 Male 0.0 0.0 0.295918 United-States \n", - "19821 Male 0.0 0.0 0.244898 United-States \n", - "15539 Male 0.0 0.0 0.500000 United-States \n", - "416 Male 0.0 0.0 0.397959 United-States " + " capital-gain capital-loss hours-per-week native-country \n", + "31082 0.0 0.0 0.397959 United-States \n", + "43423 0.0 0.0 0.142857 United-States \n", + "29225 0.0 0.0 0.500000 United-States \n", + "8419 0.0 0.0 0.397959 United-States \n", + "35362 0.0 0.0 0.418367 United-States " ] }, - "execution_count": 38, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -899,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 16, "metadata": { "scrolled": true }, @@ -914,11 +916,11 @@ "['age', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week', 'workclass_ Federal-gov', 'workclass_ Local-gov', 'workclass_ Private', 'workclass_ Self-emp-inc', 'workclass_ Self-emp-not-inc', 'workclass_ State-gov', 'workclass_ Without-pay', 'education_level_ 10th', 'education_level_ 11th', 'education_level_ 12th', 'education_level_ 1st-4th', 'education_level_ 5th-6th', 'education_level_ 7th-8th', 'education_level_ 9th', 'education_level_ Assoc-acdm', 'education_level_ Assoc-voc', 'education_level_ Bachelors', 'education_level_ Doctorate', 'education_level_ HS-grad', 'education_level_ Masters', 'education_level_ Preschool', 'education_level_ Prof-school', 'education_level_ Some-college', 'marital-status_ Divorced', 'marital-status_ Married-AF-spouse', 'marital-status_ Married-civ-spouse', 'marital-status_ Married-spouse-absent', 'marital-status_ Never-married', 'marital-status_ Separated', 'marital-status_ Widowed', 'occupation_ Adm-clerical', 'occupation_ Armed-Forces', 'occupation_ Craft-repair', 'occupation_ Exec-managerial', 'occupation_ Farming-fishing', 'occupation_ Handlers-cleaners', 'occupation_ Machine-op-inspct', 'occupation_ Other-service', 'occupation_ Priv-house-serv', 'occupation_ Prof-specialty', 'occupation_ Protective-serv', 'occupation_ Sales', 'occupation_ Tech-support', 'occupation_ Transport-moving', 'relationship_ Husband', 'relationship_ Not-in-family', 'relationship_ Other-relative', 'relationship_ Own-child', 'relationship_ Unmarried', 'relationship_ Wife', 'race_ Amer-Indian-Eskimo', 'race_ Asian-Pac-Islander', 'race_ Black', 'race_ Other', 'race_ White', 'sex_ Female', 'sex_ Male', 'native-country_ Cambodia', 'native-country_ Canada', 'native-country_ China', 'native-country_ Columbia', 'native-country_ Cuba', 'native-country_ Dominican-Republic', 'native-country_ Ecuador', 'native-country_ El-Salvador', 'native-country_ England', 'native-country_ France', 'native-country_ Germany', 'native-country_ Greece', 'native-country_ Guatemala', 'native-country_ Haiti', 'native-country_ Holand-Netherlands', 'native-country_ Honduras', 'native-country_ Hong', 'native-country_ Hungary', 'native-country_ India', 'native-country_ Iran', 'native-country_ Ireland', 'native-country_ Italy', 'native-country_ Jamaica', 'native-country_ Japan', 'native-country_ Laos', 'native-country_ Mexico', 'native-country_ Nicaragua', 'native-country_ Outlying-US(Guam-USVI-etc)', 'native-country_ Peru', 'native-country_ Philippines', 'native-country_ Poland', 'native-country_ Portugal', 'native-country_ Puerto-Rico', 'native-country_ Scotland', 'native-country_ South', 'native-country_ Taiwan', 'native-country_ Thailand', 'native-country_ Trinadad&Tobago', 'native-country_ United-States', 'native-country_ Vietnam', 'native-country_ Yugoslavia']\n", "\n", "The income col now looks like:\n", - "16481 0\n", - "37818 0\n", - "20804 0\n", - "3242 1\n", - "23475 0\n", + "43910 1\n", + "21041 1\n", + "44207 0\n", + "7311 0\n", + "26982 0\n", "Name: income, dtype: object\n" ] } @@ -949,7 +951,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -961,7 +963,7 @@ "Name: income, dtype: int32" ] }, - "execution_count": 40, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } @@ -993,7 +995,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -1042,17 +1044,41 @@ " \n", " \n", " \n", - " 16702\n", - " 0.383562\n", + " 24894\n", + " 0.123288\n", + " 0.533333\n", + " 0.0\n", + " 0.000000\n", + " 0.397959\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " ...\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 1\n", + " 0\n", + " 0\n", + " \n", + " \n", + " 8437\n", + " 0.465753\n", " 0.866667\n", " 0.0\n", - " 0.0\n", - " 0.346939\n", + " 0.000000\n", + " 0.602041\n", " 0\n", - " 1\n", " 0\n", " 0\n", " 0\n", + " 1\n", " ...\n", " 0\n", " 0\n", @@ -1066,17 +1092,17 @@ " 0\n", " \n", " \n", - " 8879\n", - " 0.520548\n", - " 0.333333\n", + " 24178\n", + " 0.000000\n", + " 0.466667\n", " 0.0\n", - " 0.0\n", - " 0.285714\n", + " 0.000000\n", + " 0.142857\n", + " 0\n", + " 0\n", " 0\n", " 0\n", " 1\n", - " 0\n", - " 0\n", " ...\n", " 0\n", " 0\n", @@ -1090,12 +1116,12 @@ " 0\n", " \n", " \n", - " 14928\n", - " 0.150685\n", + " 25776\n", + " 0.315068\n", " 0.600000\n", " 0.0\n", - " 0.0\n", - " 0.397959\n", + " 0.900201\n", + " 0.683673\n", " 0\n", " 0\n", " 1\n", @@ -1114,35 +1140,11 @@ " 0\n", " \n", " \n", - " 28087\n", - " 0.273973\n", - " 0.533333\n", - " 0.0\n", - " 0.0\n", - " 0.397959\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " ...\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 0\n", - " 1\n", - " 0\n", - " 0\n", - " \n", - " \n", - " 33970\n", - " 0.232877\n", - " 0.533333\n", - " 0.0\n", + " 17204\n", + " 0.630137\n", + " 0.266667\n", " 0.0\n", + " 0.000000\n", " 0.397959\n", " 0\n", " 0\n", @@ -1168,65 +1170,65 @@ ], "text/plain": [ " age education-num capital-gain capital-loss hours-per-week \\\n", - "16702 0.383562 0.866667 0.0 0.0 0.346939 \n", - "8879 0.520548 0.333333 0.0 0.0 0.285714 \n", - "14928 0.150685 0.600000 0.0 0.0 0.397959 \n", - "28087 0.273973 0.533333 0.0 0.0 0.397959 \n", - "33970 0.232877 0.533333 0.0 0.0 0.397959 \n", + "24894 0.123288 0.533333 0.0 0.000000 0.397959 \n", + "8437 0.465753 0.866667 0.0 0.000000 0.602041 \n", + "24178 0.000000 0.466667 0.0 0.000000 0.142857 \n", + "25776 0.315068 0.600000 0.0 0.900201 0.683673 \n", + "17204 0.630137 0.266667 0.0 0.000000 0.397959 \n", "\n", " workclass_ Federal-gov workclass_ Local-gov workclass_ Private \\\n", - "16702 0 1 0 \n", - "8879 0 0 1 \n", - "14928 0 0 1 \n", - "28087 0 0 1 \n", - "33970 0 0 1 \n", + "24894 0 0 1 \n", + "8437 0 0 0 \n", + "24178 0 0 0 \n", + "25776 0 0 1 \n", + "17204 0 0 1 \n", "\n", " workclass_ Self-emp-inc workclass_ Self-emp-not-inc ... \\\n", - "16702 0 0 ... \n", - "8879 0 0 ... \n", - "14928 0 0 ... \n", - "28087 0 0 ... \n", - "33970 0 0 ... \n", + "24894 0 0 ... \n", + "8437 0 1 ... \n", + "24178 0 1 ... \n", + "25776 0 0 ... \n", + "17204 0 0 ... \n", "\n", " native-country_ Portugal native-country_ Puerto-Rico \\\n", - "16702 0 0 \n", - "8879 0 0 \n", - "14928 0 0 \n", - "28087 0 0 \n", - "33970 0 0 \n", + "24894 0 0 \n", + "8437 0 0 \n", + "24178 0 0 \n", + "25776 0 0 \n", + "17204 0 0 \n", "\n", " native-country_ Scotland native-country_ South \\\n", - "16702 0 0 \n", - "8879 0 0 \n", - "14928 0 0 \n", - "28087 0 0 \n", - "33970 0 0 \n", + "24894 0 0 \n", + "8437 0 0 \n", + "24178 0 0 \n", + "25776 0 0 \n", + "17204 0 0 \n", "\n", " native-country_ Taiwan native-country_ Thailand \\\n", - "16702 0 0 \n", - "8879 0 0 \n", - "14928 0 0 \n", - "28087 0 0 \n", - "33970 0 0 \n", + "24894 0 0 \n", + "8437 0 0 \n", + "24178 0 0 \n", + "25776 0 0 \n", + "17204 0 0 \n", "\n", " native-country_ Trinadad&Tobago native-country_ United-States \\\n", - "16702 0 1 \n", - "8879 0 1 \n", - "14928 0 1 \n", - "28087 0 1 \n", - "33970 0 1 \n", + "24894 0 1 \n", + "8437 0 1 \n", + "24178 0 1 \n", + "25776 0 1 \n", + "17204 0 1 \n", "\n", " native-country_ Vietnam native-country_ Yugoslavia \n", - "16702 0 0 \n", - "8879 0 0 \n", - "14928 0 0 \n", - "28087 0 0 \n", - "33970 0 0 \n", + "24894 0 0 \n", + "8437 0 0 \n", + "24178 0 0 \n", + "25776 0 0 \n", + "17204 0 0 \n", "\n", "[5 rows x 103 columns]" ] }, - "execution_count": 41, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -1237,7 +1239,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -1321,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -1333,7 +1335,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -1468,7 +1470,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 22, "metadata": {}, "outputs": [ { @@ -1578,7 +1580,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1652,7 +1654,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -1666,7 +1668,7 @@ "Name: income, dtype: int32" ] }, - "execution_count": 47, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1677,7 +1679,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -1708,7 +1710,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1824,7 +1826,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 26, "metadata": {}, "outputs": [ { @@ -1836,7 +1838,7 @@ " tol=0.001, verbose=False)" ] }, - "execution_count": 51, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -1853,7 +1855,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1862,7 +1864,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -1871,7 +1873,7 @@ "0.8245439469320066" ] }, - "execution_count": 54, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } @@ -1882,29 +1884,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 20\u001b[0m \u001b[0;31m# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m \u001b[0mgrid_fit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgrid_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m# Get the estimator\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, groups, **fit_params)\u001b[0m\n\u001b[1;32m 685\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 687\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 688\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 689\u001b[0m \u001b[0;31m# For multi-metric evaluation, store the best_index_, best_params_ and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36m_run_search\u001b[0;34m(self, evaluate_candidates)\u001b[0m\n\u001b[1;32m 1146\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_run_search\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1147\u001b[0m \u001b[0;34m\"\"\"Search all candidates in param_grid\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1148\u001b[0;31m \u001b[0mevaluate_candidates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mParameterGrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparam_grid\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1149\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py\u001b[0m in \u001b[0;36mevaluate_candidates\u001b[0;34m(candidate_params)\u001b[0m\n\u001b[1;32m 664\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 665\u001b[0m in product(candidate_params,\n\u001b[0;32m--> 666\u001b[0;31m cv.split(X, y, groups)))\n\u001b[0m\u001b[1;32m 667\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 668\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, iterable)\u001b[0m\n\u001b[1;32m 932\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 933\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieval_context\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mretrieve\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 935\u001b[0m \u001b[0;31m# Make sure that we get a last message telling us we are done\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 936\u001b[0m \u001b[0melapsed_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_start_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/joblib/parallel.py\u001b[0m in \u001b[0;36mretrieve\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 831\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 832\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'supports_timeout'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 833\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 834\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 835\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_output\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mjob\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/.virtualenvs/udacity-ML-3.7.3/lib/python3.7/site-packages/joblib/_parallel_backends.py\u001b[0m in \u001b[0;36mwrap_future_result\u001b[0;34m(future, timeout)\u001b[0m\n\u001b[1;32m 519\u001b[0m AsyncResults.get from multiprocessing.\"\"\"\n\u001b[1;32m 520\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 521\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfuture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 522\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mLokyTimeoutError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 523\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mTimeoutError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib64/python3.7/concurrent/futures/_base.py\u001b[0m in \u001b[0;36mresult\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 425\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__get_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 426\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_condition\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 428\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 429\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_state\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mCANCELLED\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mCANCELLED_AND_NOTIFIED\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/usr/lib64/python3.7/threading.py\u001b[0m in \u001b[0;36mwait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 295\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mtimeout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 296\u001b[0;31m \u001b[0mwaiter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0macquire\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 297\u001b[0m \u001b[0mgotit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 298\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], + "outputs": [], "source": [ "# TODO: Import 'GridSearchCV', 'make_scorer', and any other necessary libraries\n", "from sklearn.model_selection import GridSearchCV\n", @@ -1923,7 +1905,7 @@ "scorer = make_scorer(fbeta_score, beta=0.5)\n", "\n", "# TODO: Perform grid search on the classifier using 'scorer' as the scoring method using GridSearchCV()\n", - "grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer, n_jobs=-1)\n", + "grid_obj = GridSearchCV(clf, param_grid=parameters, scoring=scorer)\n", "\n", "# TODO: Fit the grid search object to the training data and find the optimal parameters using fit()\n", "grid_fit = grid_obj.fit(X_train, y_train)\n",