completed all sections of 1

2019-07-13 20:29:45 +01:00
parent d1e1f67c6a
commit 72979f6a6f
13 changed files with 3820 additions and 0 deletions
--- a/Learning/Training
+++ b/Learning/Training
--- a/Learning/Training
+++ b/Learning/Training
--- a/Tuning/Grid_Search_Lab.ipynb
+++ b/Tuning/Grid_Search_Lab.ipynb
--- a/Tuning/Grid_Search_Lab_sol.ipynb
+++ b/Tuning/Grid_Search_Lab_sol.ipynb
--- a/Tuning/pycache/check_file.cpython-37.pyc
+++ b/Tuning/pycache/check_file.cpython-37.pyc
--- a/Tuning/check_file.py
+++ b/Tuning/check_file.py
@@ -0,0 +1,131 @@
 import pandas as pd
 import numpy as np
 from sklearn.datasets import load_diabetes
 from sklearn.model_selection import train_test_split, RandomizedSearchCV
 from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
 from sklearn.ensemble import BaggingClassifier, RandomForestClassifier, AdaBoostClassifier
 from sklearn.naive_bayes import MultinomialNB
 import matplotlib.pyplot as plt
 from sklearn.svm import SVC
 import seaborn as sns
 def check_one(answers_one):
    '''
    INPUT:
    answers_one - a dictionary with key-value pairs associated with question 1
    OUTPUT:
    nothing returned
    print a statement related to the correctness of the answers
    '''
    a = '0.65'
    b = '0'
    c = 'Age'
    d = '0.35'
    e = 'Glucose'
    f = '0.5'
    g = "More than zero"
    answers_one_1 = {
        'The proportion of diabetes outcomes in the dataset': d,
        'The number of missing data points in the dataset': b,
        'A dataset with a symmetric distribution': e,
        'A dataset with a right-skewed distribution': c,
        'This variable has the strongest correlation with the outcome': e
    }
    if answers_one == answers_one_1:
        print("Awesome! These all look great!")
    if answers_one['The proportion of diabetes outcomes in the dataset'] != answers_one_1['The proportion of diabetes outcomes in the dataset']:
        print("Oops!  That doesn't look like the proportion of 1's in the outcomes column.  I saw closer to 35% using the describe() method.")
    if answers_one['The number of missing data points in the dataset'] != answers_one_1['The number of missing data points in the dataset']:
        print("Oops!  That doesn't look like the right number of missing values.  I actually couldn't find any missing values")
    if answers_one['A dataset with a symmetric distribution'] != answers_one_1['A dataset with a symmetric distribution']:
        print("Oops!  Of the two columns above, Glucose is actually the symmetric column.  You can see this by running the .hist() method on your dataframe.")
    if answers_one['A dataset with a right-skewed distribution'] != answers_one_1['A dataset with a right-skewed distribution']:
        print("Oops!  Of the two columns above, Age is actually the right-skewed column.  You can see this by running the .hist() method on your dataframe.")
    if answers_one['This variable has the strongest correlation with the outcome'] != answers_one_1['This variable has the strongest correlation with the outcome']:
        print("Oops!  Besides Outcome itself, the column that is most correlated with the Outcome variable is actually Glucose.")
 def print_metrics(y_true, preds, model_name=None):
    '''
    INPUT:
    y_true - the y values that are actually true in the dataset (numpy array or pandas series)
    preds - the predictions for those values from some model (numpy array or pandas series)
    model_name - (str - optional) a name associated with the model if you would like to add it to the print statements
    OUTPUT:
    None - prints the accuracy, precision, recall, and F1 score
    '''
    if model_name == None:
        print('Accuracy score: ', format(accuracy_score(y_true, preds)))
        print('Precision score: ', format(precision_score(y_true, preds)))
        print('Recall score: ', format(recall_score(y_true, preds)))
        print('F1 score: ', format(f1_score(y_true, preds)))
        print('\n\n')
    else:
        print('Accuracy score for ' + model_name + ' :' , format(accuracy_score(y_true, preds)))
        print('Precision score ' + model_name + ' :', format(precision_score(y_true, preds)))
        print('Recall score ' + model_name + ' :', format(recall_score(y_true, preds)))
        print('F1 score ' + model_name + ' :', format(f1_score(y_true, preds)))
        print('\n\n')
 def check_best(best_model):
    '''
    INPUT:
    best_model - a string of the best model
    OUTPUT:
    print a statement related to if the best model matches what the solution found
    '''
    a = 'randomforest'
    b = 'adaboost'
    c = 'supportvector'
    if best_model == b:
        print("Nice!  It looks like your best model matches the best model I found as well!  It makes sense to use f1 score to determine best in this case given the imbalance of classes.  There might be justification for precision or recall being the best metric to use as well - precision showed to be best with adaboost again.  With recall, SVMs proved to be the best for our models.")
    else:
        print("That wasn't the model I had in mind... It makes sense to use f1 score to determine best in this case given the imbalance of classes.  There could also be justification for precision or recall being the best metric to use as well - precision showed to be best with adaboost.  With recall, SVMs proved to be the best for our models.")
 def check_q_seven(sol_seven):
    '''
    INPUT:
    solution dictionary for part seven
    OUTPUT:
    prints statement related to correctness of dictionary
    '''
    a = 'Age'
    b = 'BloodPressure'
    c = 'BMI'
    d = 'DiabetesPedigreeFunction'
    e = 'Insulin'
    f = 'Glucose'
    g = 'Pregnancy'
    h = 'SkinThickness'
    sol_seven_1 = {
        'The variable that is most related to the outcome of diabetes' : f,
        'The second most related variable to the outcome of diabetes' : c,
        'The third most related variable to the outcome of diabetes' : a,
        'The fourth most related variable to the outcome of diabetes' : d
    }
    if sol_seven == sol_seven_1:
        print("That's right!  Some of these were expected, but some were a bit unexpected too!")
    else:
        print("That doesn't look like what I expected, but maybe your feature importances were different - that can definitely happen.  Take a look at the best_estimator_.feature_importances_ portion of your fitted model.")
--- a/Learning/Training
+++ b/Learning/Training
@@ -0,0 +1,101 @@
 x1,x2,y
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--- a/Tuning/data_grid.csv
+++ b/Tuning/data_grid.csv
@@ -0,0 +1,100 @@
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--- a/Tuning/detecting_quiz.py
+++ b/Tuning/detecting_quiz.py
@@ -0,0 +1,83 @@
 # Import, read, and split data
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import LogisticRegression
 from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.svm import SVC
 from sklearn.model_selection import learning_curve
 from sklearn.model_selection import ShuffleSplit
 data = pd.read_csv('data.csv')
 X = np.array(data[['x1', 'x2']])
 y = np.array(data['y'])
 # Fix random seed
 np.random.seed(55)
 # TODO: Uncomment one of the three classifiers, and hit "Test Run"
 # to see the learning curve. Use these to answer the quiz below.
 # Logistic Regression
 estimator0 = LogisticRegression(solver='lbfgs')
 # Decision Tree
 estimator1 = GradientBoostingClassifier()
 # Support Vector Machine
 estimator2 = SVC(kernel='rbf', gamma=1000)
 def randomize(X, Y):
    permutation = np.random.permutation(Y.shape[0])
    X2 = X[permutation, :]
    Y2 = Y[permutation]
    return X2, Y2
 X2, y2 = randomize(X, y)
 def draw_learning_curves(X, y, estimator, num_trainings):
    cv = ShuffleSplit(n_splits=5, test_size=0.3)
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X2, y2, cv=cv, n_jobs=1,
        train_sizes=np.linspace(.1, 1.0, num_trainings))
    print(test_scores)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()
    plt.title("Learning Curves")
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    # plt.plot(train_scores_mean, 'o-', color="g",
    #          label="Training score")
    # plt.plot(test_scores_mean, 'o-', color="y",
    #          label="Cross-validation score")
    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.1,
                     color='r')
    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.1,
                     color='g')
    plt.plot(train_sizes, train_scores_mean, 'o-', color='r',
             label='Training Score')
    plt.plot(train_sizes, test_scores_mean, 'o-', color='g',
             label='Cross-Validation Score')
    plt.legend(loc="best")
    plt.show()
 draw_learning_curves(X2, y2, estimator2, 10)
 cv = ShuffleSplit(n_splits=5, test_size=0.3)
 print(cv)
 for i, j in cv.split(X2):
    print(i, j)
--- a/Tuning/diabetes.csv
+++ b/Tuning/diabetes.csv
@@ -0,0 +1,769 @@
 Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
 6,148,72,35,0,33.6,0.627,50,1
 1,85,66,29,0,26.6,0.351,31,0
 8,183,64,0,0,23.3,0.672,32,1
 1,89,66,23,94,28.1,0.167,21,0
 0,137,40,35,168,43.1,2.288,33,1
 5,116,74,0,0,25.6,0.201,30,0
 3,78,50,32,88,31,0.248,26,1
 10,115,0,0,0,35.3,0.134,29,0
 2,197,70,45,543,30.5,0.158,53,1
 8,125,96,0,0,0,0.232,54,1
 4,110,92,0,0,37.6,0.191,30,0
 10,168,74,0,0,38,0.537,34,1
 10,139,80,0,0,27.1,1.441,57,0
 1,189,60,23,846,30.1,0.398,59,1
 5,166,72,19,175,25.8,0.587,51,1
 7,100,0,0,0,30,0.484,32,1
 0,118,84,47,230,45.8,0.551,31,1
 7,107,74,0,0,29.6,0.254,31,1
 1,103,30,38,83,43.3,0.183,33,0
 1,115,70,30,96,34.6,0.529,32,1
 3,126,88,41,235,39.3,0.704,27,0
 8,99,84,0,0,35.4,0.388,50,0
 7,196,90,0,0,39.8,0.451,41,1
 9,119,80,35,0,29,0.263,29,1
 11,143,94,33,146,36.6,0.254,51,1
 10,125,70,26,115,31.1,0.205,41,1
 7,147,76,0,0,39.4,0.257,43,1
 1,97,66,15,140,23.2,0.487,22,0
 13,145,82,19,110,22.2,0.245,57,0
 5,117,92,0,0,34.1,0.337,38,0
 5,109,75,26,0,36,0.546,60,0
 3,158,76,36,245,31.6,0.851,28,1
 3,88,58,11,54,24.8,0.267,22,0
 6,92,92,0,0,19.9,0.188,28,0
 10,122,78,31,0,27.6,0.512,45,0
 4,103,60,33,192,24,0.966,33,0
 11,138,76,0,0,33.2,0.42,35,0
 9,102,76,37,0,32.9,0.665,46,1
 2,90,68,42,0,38.2,0.503,27,1
 4,111,72,47,207,37.1,1.39,56,1
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 7,133,84,0,0,40.2,0.696,37,0
 7,106,92,18,0,22.7,0.235,48,0
 9,171,110,24,240,45.4,0.721,54,1
 7,159,64,0,0,27.4,0.294,40,0
 0,180,66,39,0,42,1.893,25,1
 1,146,56,0,0,29.7,0.564,29,0
 2,71,70,27,0,28,0.586,22,0
 7,103,66,32,0,39.1,0.344,31,1
 7,105,0,0,0,0,0.305,24,0
 1,103,80,11,82,19.4,0.491,22,0
 1,101,50,15,36,24.2,0.526,26,0
 5,88,66,21,23,24.4,0.342,30,0
 8,176,90,34,300,33.7,0.467,58,1
 7,150,66,42,342,34.7,0.718,42,0
 1,73,50,10,0,23,0.248,21,0
 7,187,68,39,304,37.7,0.254,41,1
 0,100,88,60,110,46.8,0.962,31,0
 0,146,82,0,0,40.5,1.781,44,0
 0,105,64,41,142,41.5,0.173,22,0
 2,84,0,0,0,0,0.304,21,0
 8,133,72,0,0,32.9,0.27,39,1
 5,44,62,0,0,25,0.587,36,0
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 7,114,66,0,0,32.8,0.258,42,1
 5,99,74,27,0,29,0.203,32,0
 0,109,88,30,0,32.5,0.855,38,1
 2,109,92,0,0,42.7,0.845,54,0
 1,95,66,13,38,19.6,0.334,25,0
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 2,100,66,20,90,32.9,0.867,28,1
 5,139,64,35,140,28.6,0.411,26,0
 13,126,90,0,0,43.4,0.583,42,1
 4,129,86,20,270,35.1,0.231,23,0
 1,79,75,30,0,32,0.396,22,0
 1,0,48,20,0,24.7,0.14,22,0
 7,62,78,0,0,32.6,0.391,41,0
 5,95,72,33,0,37.7,0.37,27,0
 0,131,0,0,0,43.2,0.27,26,1
 2,112,66,22,0,25,0.307,24,0
 3,113,44,13,0,22.4,0.14,22,0
 2,74,0,0,0,0,0.102,22,0
 7,83,78,26,71,29.3,0.767,36,0
 0,101,65,28,0,24.6,0.237,22,0
 5,137,108,0,0,48.8,0.227,37,1
 2,110,74,29,125,32.4,0.698,27,0
 13,106,72,54,0,36.6,0.178,45,0
 2,100,68,25,71,38.5,0.324,26,0
 15,136,70,32,110,37.1,0.153,43,1
 1,107,68,19,0,26.5,0.165,24,0
 1,80,55,0,0,19.1,0.258,21,0
 4,123,80,15,176,32,0.443,34,0
 7,81,78,40,48,46.7,0.261,42,0
 4,134,72,0,0,23.8,0.277,60,1
 2,142,82,18,64,24.7,0.761,21,0
 6,144,72,27,228,33.9,0.255,40,0
 2,92,62,28,0,31.6,0.13,24,0
 1,71,48,18,76,20.4,0.323,22,0
 6,93,50,30,64,28.7,0.356,23,0
 1,122,90,51,220,49.7,0.325,31,1
 1,163,72,0,0,39,1.222,33,1
 1,151,60,0,0,26.1,0.179,22,0
 0,125,96,0,0,22.5,0.262,21,0
 1,81,72,18,40,26.6,0.283,24,0
 2,85,65,0,0,39.6,0.93,27,0
 1,126,56,29,152,28.7,0.801,21,0
 1,96,122,0,0,22.4,0.207,27,0
 4,144,58,28,140,29.5,0.287,37,0
 3,83,58,31,18,34.3,0.336,25,0
 0,95,85,25,36,37.4,0.247,24,1
 3,171,72,33,135,33.3,0.199,24,1
 8,155,62,26,495,34,0.543,46,1
 1,89,76,34,37,31.2,0.192,23,0
 4,76,62,0,0,34,0.391,25,0
 7,160,54,32,175,30.5,0.588,39,1
 4,146,92,0,0,31.2,0.539,61,1
 5,124,74,0,0,34,0.22,38,1
 5,78,48,0,0,33.7,0.654,25,0
 4,97,60,23,0,28.2,0.443,22,0
 4,99,76,15,51,23.2,0.223,21,0
 0,162,76,56,100,53.2,0.759,25,1
 6,111,64,39,0,34.2,0.26,24,0
 2,107,74,30,100,33.6,0.404,23,0
 5,132,80,0,0,26.8,0.186,69,0
 0,113,76,0,0,33.3,0.278,23,1
 1,88,30,42,99,55,0.496,26,1
 3,120,70,30,135,42.9,0.452,30,0
 1,118,58,36,94,33.3,0.261,23,0
 1,117,88,24,145,34.5,0.403,40,1
 0,105,84,0,0,27.9,0.741,62,1
 4,173,70,14,168,29.7,0.361,33,1
 9,122,56,0,0,33.3,1.114,33,1
 3,170,64,37,225,34.5,0.356,30,1
 8,84,74,31,0,38.3,0.457,39,0
 2,96,68,13,49,21.1,0.647,26,0
 2,125,60,20,140,33.8,0.088,31,0
 0,100,70,26,50,30.8,0.597,21,0
 0,93,60,25,92,28.7,0.532,22,0
 0,129,80,0,0,31.2,0.703,29,0
 5,105,72,29,325,36.9,0.159,28,0
 3,128,78,0,0,21.1,0.268,55,0
 5,106,82,30,0,39.5,0.286,38,0
 2,108,52,26,63,32.5,0.318,22,0
 10,108,66,0,0,32.4,0.272,42,1
 4,154,62,31,284,32.8,0.237,23,0
 0,102,75,23,0,0,0.572,21,0
 9,57,80,37,0,32.8,0.096,41,0
 2,106,64,35,119,30.5,1.4,34,0
 5,147,78,0,0,33.7,0.218,65,0
 2,90,70,17,0,27.3,0.085,22,0
 1,136,74,50,204,37.4,0.399,24,0
 4,114,65,0,0,21.9,0.432,37,0
 9,156,86,28,155,34.3,1.189,42,1
 1,153,82,42,485,40.6,0.687,23,0
 8,188,78,0,0,47.9,0.137,43,1
 7,152,88,44,0,50,0.337,36,1
 2,99,52,15,94,24.6,0.637,21,0
 1,109,56,21,135,25.2,0.833,23,0
 2,88,74,19,53,29,0.229,22,0
 17,163,72,41,114,40.9,0.817,47,1
 4,151,90,38,0,29.7,0.294,36,0
 7,102,74,40,105,37.2,0.204,45,0
 0,114,80,34,285,44.2,0.167,27,0
 2,100,64,23,0,29.7,0.368,21,0
 0,131,88,0,0,31.6,0.743,32,1
 6,104,74,18,156,29.9,0.722,41,1
 3,148,66,25,0,32.5,0.256,22,0
 4,120,68,0,0,29.6,0.709,34,0
 4,110,66,0,0,31.9,0.471,29,0
 3,111,90,12,78,28.4,0.495,29,0
 6,102,82,0,0,30.8,0.18,36,1
 6,134,70,23,130,35.4,0.542,29,1
 2,87,0,23,0,28.9,0.773,25,0
 1,79,60,42,48,43.5,0.678,23,0
 2,75,64,24,55,29.7,0.37,33,0
 8,179,72,42,130,32.7,0.719,36,1
 6,85,78,0,0,31.2,0.382,42,0
 0,129,110,46,130,67.1,0.319,26,1
 5,143,78,0,0,45,0.19,47,0
 5,130,82,0,0,39.1,0.956,37,1
 6,87,80,0,0,23.2,0.084,32,0
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 5,73,60,0,0,26.8,0.268,27,0
 4,141,74,0,0,27.6,0.244,40,0
 7,194,68,28,0,35.9,0.745,41,1
 8,181,68,36,495,30.1,0.615,60,1
 1,128,98,41,58,32,1.321,33,1
 8,109,76,39,114,27.9,0.64,31,1
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 3,111,62,0,0,22.6,0.142,21,0
 9,123,70,44,94,33.1,0.374,40,0
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 11,135,0,0,0,52.3,0.578,40,1
 8,85,55,20,0,24.4,0.136,42,0
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 1,105,58,0,0,24.3,0.187,21,0
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 4,109,64,44,99,34.8,0.905,26,1
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 0,108,68,20,0,27.3,0.787,32,0
 2,99,70,16,44,20.4,0.235,27,0
 6,103,72,32,190,37.7,0.324,55,0
 5,111,72,28,0,23.9,0.407,27,0
 8,196,76,29,280,37.5,0.605,57,1
 5,162,104,0,0,37.7,0.151,52,1
 1,96,64,27,87,33.2,0.289,21,0
 7,184,84,33,0,35.5,0.355,41,1
 2,81,60,22,0,27.7,0.29,25,0
 0,147,85,54,0,42.8,0.375,24,0
 7,179,95,31,0,34.2,0.164,60,0
 0,140,65,26,130,42.6,0.431,24,1
 9,112,82,32,175,34.2,0.26,36,1
 12,151,70,40,271,41.8,0.742,38,1
 5,109,62,41,129,35.8,0.514,25,1
 6,125,68,30,120,30,0.464,32,0
 5,85,74,22,0,29,1.224,32,1
 5,112,66,0,0,37.8,0.261,41,1
 0,177,60,29,478,34.6,1.072,21,1
 2,158,90,0,0,31.6,0.805,66,1
 7,119,0,0,0,25.2,0.209,37,0
 7,142,60,33,190,28.8,0.687,61,0
 1,100,66,15,56,23.6,0.666,26,0
 1,87,78,27,32,34.6,0.101,22,0
 0,101,76,0,0,35.7,0.198,26,0
 3,162,52,38,0,37.2,0.652,24,1
 4,197,70,39,744,36.7,2.329,31,0
 0,117,80,31,53,45.2,0.089,24,0
 4,142,86,0,0,44,0.645,22,1
 6,134,80,37,370,46.2,0.238,46,1
 1,79,80,25,37,25.4,0.583,22,0
 4,122,68,0,0,35,0.394,29,0
 3,74,68,28,45,29.7,0.293,23,0
 4,171,72,0,0,43.6,0.479,26,1
 7,181,84,21,192,35.9,0.586,51,1
 0,179,90,27,0,44.1,0.686,23,1
 9,164,84,21,0,30.8,0.831,32,1
 0,104,76,0,0,18.4,0.582,27,0
 1,91,64,24,0,29.2,0.192,21,0
 4,91,70,32,88,33.1,0.446,22,0
 3,139,54,0,0,25.6,0.402,22,1
 6,119,50,22,176,27.1,1.318,33,1
 2,146,76,35,194,38.2,0.329,29,0
 9,184,85,15,0,30,1.213,49,1
 10,122,68,0,0,31.2,0.258,41,0
 0,165,90,33,680,52.3,0.427,23,0
 9,124,70,33,402,35.4,0.282,34,0
 1,111,86,19,0,30.1,0.143,23,0
 9,106,52,0,0,31.2,0.38,42,0
 2,129,84,0,0,28,0.284,27,0
 2,90,80,14,55,24.4,0.249,24,0
 0,86,68,32,0,35.8,0.238,25,0
 12,92,62,7,258,27.6,0.926,44,1
 1,113,64,35,0,33.6,0.543,21,1
 3,111,56,39,0,30.1,0.557,30,0
 2,114,68,22,0,28.7,0.092,25,0
 1,193,50,16,375,25.9,0.655,24,0
 11,155,76,28,150,33.3,1.353,51,1
 3,191,68,15,130,30.9,0.299,34,0
 3,141,0,0,0,30,0.761,27,1
 4,95,70,32,0,32.1,0.612,24,0
 3,142,80,15,0,32.4,0.2,63,0
 4,123,62,0,0,32,0.226,35,1
 5,96,74,18,67,33.6,0.997,43,0
 0,138,0,0,0,36.3,0.933,25,1
 2,128,64,42,0,40,1.101,24,0
 0,102,52,0,0,25.1,0.078,21,0
 2,146,0,0,0,27.5,0.24,28,1
 10,101,86,37,0,45.6,1.136,38,1
 2,108,62,32,56,25.2,0.128,21,0
 3,122,78,0,0,23,0.254,40,0
 1,71,78,50,45,33.2,0.422,21,0
 13,106,70,0,0,34.2,0.251,52,0
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 7,106,60,24,0,26.5,0.296,29,1
 0,104,64,23,116,27.8,0.454,23,0
 5,114,74,0,0,24.9,0.744,57,0
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 0,146,70,0,0,37.9,0.334,28,1
 10,129,76,28,122,35.9,0.28,39,0
 7,133,88,15,155,32.4,0.262,37,0
 7,161,86,0,0,30.4,0.165,47,1
 2,108,80,0,0,27,0.259,52,1
 7,136,74,26,135,26,0.647,51,0
 5,155,84,44,545,38.7,0.619,34,0
 1,119,86,39,220,45.6,0.808,29,1
 4,96,56,17,49,20.8,0.34,26,0
 5,108,72,43,75,36.1,0.263,33,0
 0,78,88,29,40,36.9,0.434,21,0
 0,107,62,30,74,36.6,0.757,25,1
 2,128,78,37,182,43.3,1.224,31,1
 1,128,48,45,194,40.5,0.613,24,1
 0,161,50,0,0,21.9,0.254,65,0
 6,151,62,31,120,35.5,0.692,28,0
 2,146,70,38,360,28,0.337,29,1
 0,126,84,29,215,30.7,0.52,24,0
 14,100,78,25,184,36.6,0.412,46,1
 8,112,72,0,0,23.6,0.84,58,0
 0,167,0,0,0,32.3,0.839,30,1
 2,144,58,33,135,31.6,0.422,25,1
 5,77,82,41,42,35.8,0.156,35,0
 5,115,98,0,0,52.9,0.209,28,1
 3,150,76,0,0,21,0.207,37,0
 2,120,76,37,105,39.7,0.215,29,0
 10,161,68,23,132,25.5,0.326,47,1
 0,137,68,14,148,24.8,0.143,21,0
 0,128,68,19,180,30.5,1.391,25,1
 2,124,68,28,205,32.9,0.875,30,1
 6,80,66,30,0,26.2,0.313,41,0
 0,106,70,37,148,39.4,0.605,22,0
 2,155,74,17,96,26.6,0.433,27,1
 3,113,50,10,85,29.5,0.626,25,0
 7,109,80,31,0,35.9,1.127,43,1
 2,112,68,22,94,34.1,0.315,26,0
 3,99,80,11,64,19.3,0.284,30,0
 3,182,74,0,0,30.5,0.345,29,1
 3,115,66,39,140,38.1,0.15,28,0
 6,194,78,0,0,23.5,0.129,59,1
 4,129,60,12,231,27.5,0.527,31,0
 3,112,74,30,0,31.6,0.197,25,1
 0,124,70,20,0,27.4,0.254,36,1
 13,152,90,33,29,26.8,0.731,43,1
 2,112,75,32,0,35.7,0.148,21,0
 1,157,72,21,168,25.6,0.123,24,0
 1,122,64,32,156,35.1,0.692,30,1
 10,179,70,0,0,35.1,0.2,37,0
 2,102,86,36,120,45.5,0.127,23,1
 6,105,70,32,68,30.8,0.122,37,0
 8,118,72,19,0,23.1,1.476,46,0
 2,87,58,16,52,32.7,0.166,25,0
 1,180,0,0,0,43.3,0.282,41,1
 12,106,80,0,0,23.6,0.137,44,0
 1,95,60,18,58,23.9,0.26,22,0
 0,165,76,43,255,47.9,0.259,26,0
 0,117,0,0,0,33.8,0.932,44,0
 5,115,76,0,0,31.2,0.343,44,1
 9,152,78,34,171,34.2,0.893,33,1
 7,178,84,0,0,39.9,0.331,41,1
 1,130,70,13,105,25.9,0.472,22,0
 1,95,74,21,73,25.9,0.673,36,0
 1,0,68,35,0,32,0.389,22,0
 5,122,86,0,0,34.7,0.29,33,0
 8,95,72,0,0,36.8,0.485,57,0
 8,126,88,36,108,38.5,0.349,49,0
 1,139,46,19,83,28.7,0.654,22,0
 3,116,0,0,0,23.5,0.187,23,0
 3,99,62,19,74,21.8,0.279,26,0
 5,0,80,32,0,41,0.346,37,1
 4,92,80,0,0,42.2,0.237,29,0
 4,137,84,0,0,31.2,0.252,30,0
 3,61,82,28,0,34.4,0.243,46,0
 1,90,62,12,43,27.2,0.58,24,0
 3,90,78,0,0,42.7,0.559,21,0
 9,165,88,0,0,30.4,0.302,49,1
 1,125,50,40,167,33.3,0.962,28,1
 13,129,0,30,0,39.9,0.569,44,1
 12,88,74,40,54,35.3,0.378,48,0
 1,196,76,36,249,36.5,0.875,29,1
 5,189,64,33,325,31.2,0.583,29,1
 5,158,70,0,0,29.8,0.207,63,0
 5,103,108,37,0,39.2,0.305,65,0
 4,146,78,0,0,38.5,0.52,67,1
 4,147,74,25,293,34.9,0.385,30,0
 5,99,54,28,83,34,0.499,30,0
 6,124,72,0,0,27.6,0.368,29,1
 0,101,64,17,0,21,0.252,21,0
 3,81,86,16,66,27.5,0.306,22,0
 1,133,102,28,140,32.8,0.234,45,1
 3,173,82,48,465,38.4,2.137,25,1
 0,118,64,23,89,0,1.731,21,0
 0,84,64,22,66,35.8,0.545,21,0
 2,105,58,40,94,34.9,0.225,25,0
 2,122,52,43,158,36.2,0.816,28,0
 12,140,82,43,325,39.2,0.528,58,1
 0,98,82,15,84,25.2,0.299,22,0
 1,87,60,37,75,37.2,0.509,22,0
 4,156,75,0,0,48.3,0.238,32,1
 0,93,100,39,72,43.4,1.021,35,0
 1,107,72,30,82,30.8,0.821,24,0
 0,105,68,22,0,20,0.236,22,0
 1,109,60,8,182,25.4,0.947,21,0
 1,90,62,18,59,25.1,1.268,25,0
 1,125,70,24,110,24.3,0.221,25,0
 1,119,54,13,50,22.3,0.205,24,0
 5,116,74,29,0,32.3,0.66,35,1
 8,105,100,36,0,43.3,0.239,45,1
 5,144,82,26,285,32,0.452,58,1
 3,100,68,23,81,31.6,0.949,28,0
 1,100,66,29,196,32,0.444,42,0
 5,166,76,0,0,45.7,0.34,27,1
 1,131,64,14,415,23.7,0.389,21,0
 4,116,72,12,87,22.1,0.463,37,0
 4,158,78,0,0,32.9,0.803,31,1
 2,127,58,24,275,27.7,1.6,25,0
 3,96,56,34,115,24.7,0.944,39,0
 0,131,66,40,0,34.3,0.196,22,1
 3,82,70,0,0,21.1,0.389,25,0
 3,193,70,31,0,34.9,0.241,25,1
 4,95,64,0,0,32,0.161,31,1
 6,137,61,0,0,24.2,0.151,55,0
 5,136,84,41,88,35,0.286,35,1
 9,72,78,25,0,31.6,0.28,38,0
 5,168,64,0,0,32.9,0.135,41,1
 2,123,48,32,165,42.1,0.52,26,0
 4,115,72,0,0,28.9,0.376,46,1
 0,101,62,0,0,21.9,0.336,25,0
 8,197,74,0,0,25.9,1.191,39,1
 1,172,68,49,579,42.4,0.702,28,1
 6,102,90,39,0,35.7,0.674,28,0
 1,112,72,30,176,34.4,0.528,25,0
 1,143,84,23,310,42.4,1.076,22,0
 1,143,74,22,61,26.2,0.256,21,0
 0,138,60,35,167,34.6,0.534,21,1
 3,173,84,33,474,35.7,0.258,22,1
 1,97,68,21,0,27.2,1.095,22,0
 4,144,82,32,0,38.5,0.554,37,1
 1,83,68,0,0,18.2,0.624,27,0
 3,129,64,29,115,26.4,0.219,28,1
 1,119,88,41,170,45.3,0.507,26,0
 2,94,68,18,76,26,0.561,21,0
 0,102,64,46,78,40.6,0.496,21,0
 2,115,64,22,0,30.8,0.421,21,0
 8,151,78,32,210,42.9,0.516,36,1
 4,184,78,39,277,37,0.264,31,1
 0,94,0,0,0,0,0.256,25,0
 1,181,64,30,180,34.1,0.328,38,1
 0,135,94,46,145,40.6,0.284,26,0
 1,95,82,25,180,35,0.233,43,1
 2,99,0,0,0,22.2,0.108,23,0
 3,89,74,16,85,30.4,0.551,38,0
 1,80,74,11,60,30,0.527,22,0
 2,139,75,0,0,25.6,0.167,29,0
 1,90,68,8,0,24.5,1.138,36,0
 0,141,0,0,0,42.4,0.205,29,1
 12,140,85,33,0,37.4,0.244,41,0
 5,147,75,0,0,29.9,0.434,28,0
 1,97,70,15,0,18.2,0.147,21,0
 6,107,88,0,0,36.8,0.727,31,0
 0,189,104,25,0,34.3,0.435,41,1
 2,83,66,23,50,32.2,0.497,22,0
 4,117,64,27,120,33.2,0.23,24,0
 8,108,70,0,0,30.5,0.955,33,1
 4,117,62,12,0,29.7,0.38,30,1
 0,180,78,63,14,59.4,2.42,25,1
 1,100,72,12,70,25.3,0.658,28,0
 0,95,80,45,92,36.5,0.33,26,0
 0,104,64,37,64,33.6,0.51,22,1
 0,120,74,18,63,30.5,0.285,26,0
 1,82,64,13,95,21.2,0.415,23,0
 2,134,70,0,0,28.9,0.542,23,1
 0,91,68,32,210,39.9,0.381,25,0
 2,119,0,0,0,19.6,0.832,72,0
 2,100,54,28,105,37.8,0.498,24,0
 14,175,62,30,0,33.6,0.212,38,1
 1,135,54,0,0,26.7,0.687,62,0
 5,86,68,28,71,30.2,0.364,24,0
 10,148,84,48,237,37.6,1.001,51,1
 9,134,74,33,60,25.9,0.46,81,0
 9,120,72,22,56,20.8,0.733,48,0
 1,71,62,0,0,21.8,0.416,26,0
 8,74,70,40,49,35.3,0.705,39,0
 5,88,78,30,0,27.6,0.258,37,0
 10,115,98,0,0,24,1.022,34,0
 0,124,56,13,105,21.8,0.452,21,0
 0,74,52,10,36,27.8,0.269,22,0
 0,97,64,36,100,36.8,0.6,25,0
 8,120,0,0,0,30,0.183,38,1
 6,154,78,41,140,46.1,0.571,27,0
 1,144,82,40,0,41.3,0.607,28,0
 0,137,70,38,0,33.2,0.17,22,0
 0,119,66,27,0,38.8,0.259,22,0
 7,136,90,0,0,29.9,0.21,50,0
 4,114,64,0,0,28.9,0.126,24,0
 0,137,84,27,0,27.3,0.231,59,0
 2,105,80,45,191,33.7,0.711,29,1
 7,114,76,17,110,23.8,0.466,31,0
 8,126,74,38,75,25.9,0.162,39,0
 4,132,86,31,0,28,0.419,63,0
 3,158,70,30,328,35.5,0.344,35,1
 0,123,88,37,0,35.2,0.197,29,0
 4,85,58,22,49,27.8,0.306,28,0
 0,84,82,31,125,38.2,0.233,23,0
 0,145,0,0,0,44.2,0.63,31,1
 0,135,68,42,250,42.3,0.365,24,1
 1,139,62,41,480,40.7,0.536,21,0
 0,173,78,32,265,46.5,1.159,58,0
 4,99,72,17,0,25.6,0.294,28,0
 8,194,80,0,0,26.1,0.551,67,0
 2,83,65,28,66,36.8,0.629,24,0
 2,89,90,30,0,33.5,0.292,42,0
 4,99,68,38,0,32.8,0.145,33,0
 4,125,70,18,122,28.9,1.144,45,1
 3,80,0,0,0,0,0.174,22,0
 6,166,74,0,0,26.6,0.304,66,0
 5,110,68,0,0,26,0.292,30,0
 2,81,72,15,76,30.1,0.547,25,0
 7,195,70,33,145,25.1,0.163,55,1
 6,154,74,32,193,29.3,0.839,39,0
 2,117,90,19,71,25.2,0.313,21,0
 3,84,72,32,0,37.2,0.267,28,0
 6,0,68,41,0,39,0.727,41,1
 7,94,64,25,79,33.3,0.738,41,0
 3,96,78,39,0,37.3,0.238,40,0
 10,75,82,0,0,33.3,0.263,38,0
 0,180,90,26,90,36.5,0.314,35,1
 1,130,60,23,170,28.6,0.692,21,0
 2,84,50,23,76,30.4,0.968,21,0
 8,120,78,0,0,25,0.409,64,0
 12,84,72,31,0,29.7,0.297,46,1
 0,139,62,17,210,22.1,0.207,21,0
 9,91,68,0,0,24.2,0.2,58,0
 2,91,62,0,0,27.3,0.525,22,0
 3,99,54,19,86,25.6,0.154,24,0
 3,163,70,18,105,31.6,0.268,28,1
 9,145,88,34,165,30.3,0.771,53,1
 7,125,86,0,0,37.6,0.304,51,0
 13,76,60,0,0,32.8,0.18,41,0
 6,129,90,7,326,19.6,0.582,60,0
 2,68,70,32,66,25,0.187,25,0
 3,124,80,33,130,33.2,0.305,26,0
 6,114,0,0,0,0,0.189,26,0
 9,130,70,0,0,34.2,0.652,45,1
 3,125,58,0,0,31.6,0.151,24,0
 3,87,60,18,0,21.8,0.444,21,0
 1,97,64,19,82,18.2,0.299,21,0
 3,116,74,15,105,26.3,0.107,24,0
 0,117,66,31,188,30.8,0.493,22,0
 0,111,65,0,0,24.6,0.66,31,0
 2,122,60,18,106,29.8,0.717,22,0
 0,107,76,0,0,45.3,0.686,24,0
 1,86,66,52,65,41.3,0.917,29,0
 6,91,0,0,0,29.8,0.501,31,0
 1,77,56,30,56,33.3,1.251,24,0
 4,132,0,0,0,32.9,0.302,23,1
 0,105,90,0,0,29.6,0.197,46,0
 0,57,60,0,0,21.7,0.735,67,0
 0,127,80,37,210,36.3,0.804,23,0
 3,129,92,49,155,36.4,0.968,32,1
 8,100,74,40,215,39.4,0.661,43,1
 3,128,72,25,190,32.4,0.549,27,1
 10,90,85,32,0,34.9,0.825,56,1
 4,84,90,23,56,39.5,0.159,25,0
 1,88,78,29,76,32,0.365,29,0
 8,186,90,35,225,34.5,0.423,37,1
 5,187,76,27,207,43.6,1.034,53,1
 4,131,68,21,166,33.1,0.16,28,0
 1,164,82,43,67,32.8,0.341,50,0
 4,189,110,31,0,28.5,0.68,37,0
 1,116,70,28,0,27.4,0.204,21,0
 3,84,68,30,106,31.9,0.591,25,0
 6,114,88,0,0,27.8,0.247,66,0
 1,88,62,24,44,29.9,0.422,23,0
 1,84,64,23,115,36.9,0.471,28,0
 7,124,70,33,215,25.5,0.161,37,0
 1,97,70,40,0,38.1,0.218,30,0
 8,110,76,0,0,27.8,0.237,58,0
 11,103,68,40,0,46.2,0.126,42,0
 11,85,74,0,0,30.1,0.3,35,0
 6,125,76,0,0,33.8,0.121,54,1
 0,198,66,32,274,41.3,0.502,28,1
 1,87,68,34,77,37.6,0.401,24,0
 6,99,60,19,54,26.9,0.497,32,0
 0,91,80,0,0,32.4,0.601,27,0
 2,95,54,14,88,26.1,0.748,22,0
 1,99,72,30,18,38.6,0.412,21,0
 6,92,62,32,126,32,0.085,46,0
 4,154,72,29,126,31.3,0.338,37,0
 0,121,66,30,165,34.3,0.203,33,1
 3,78,70,0,0,32.5,0.27,39,0
 2,130,96,0,0,22.6,0.268,21,0
 3,111,58,31,44,29.5,0.43,22,0
 2,98,60,17,120,34.7,0.198,22,0
 1,143,86,30,330,30.1,0.892,23,0
 1,119,44,47,63,35.5,0.28,25,0
 6,108,44,20,130,24,0.813,35,0
 2,118,80,0,0,42.9,0.693,21,1
 10,133,68,0,0,27,0.245,36,0
 2,197,70,99,0,34.7,0.575,62,1
 0,151,90,46,0,42.1,0.371,21,1
 6,109,60,27,0,25,0.206,27,0
 12,121,78,17,0,26.5,0.259,62,0
 8,100,76,0,0,38.7,0.19,42,0
 8,124,76,24,600,28.7,0.687,52,1
 1,93,56,11,0,22.5,0.417,22,0
 8,143,66,0,0,34.9,0.129,41,1
 6,103,66,0,0,24.3,0.249,29,0
 3,176,86,27,156,33.3,1.154,52,1
 0,73,0,0,0,21.1,0.342,25,0
 11,111,84,40,0,46.8,0.925,45,1
 2,112,78,50,140,39.4,0.175,24,0
 3,132,80,0,0,34.4,0.402,44,1
 2,82,52,22,115,28.5,1.699,25,0
 6,123,72,45,230,33.6,0.733,34,0
 0,188,82,14,185,32,0.682,22,1
 0,67,76,0,0,45.3,0.194,46,0
 1,89,24,19,25,27.8,0.559,21,0
 1,173,74,0,0,36.8,0.088,38,1
 1,109,38,18,120,23.1,0.407,26,0
 1,108,88,19,0,27.1,0.4,24,0
 6,96,0,0,0,23.7,0.19,28,0
 1,124,74,36,0,27.8,0.1,30,0
 7,150,78,29,126,35.2,0.692,54,1
 4,183,0,0,0,28.4,0.212,36,1
 1,124,60,32,0,35.8,0.514,21,0
 1,181,78,42,293,40,1.258,22,1
 1,92,62,25,41,19.5,0.482,25,0
 0,152,82,39,272,41.5,0.27,27,0
 1,111,62,13,182,24,0.138,23,0
 3,106,54,21,158,30.9,0.292,24,0
 3,174,58,22,194,32.9,0.593,36,1
 7,168,88,42,321,38.2,0.787,40,1
 6,105,80,28,0,32.5,0.878,26,0
 11,138,74,26,144,36.1,0.557,50,1
 3,106,72,0,0,25.8,0.207,27,0
 6,117,96,0,0,28.7,0.157,30,0
 2,68,62,13,15,20.1,0.257,23,0
 9,112,82,24,0,28.2,1.282,50,1
 0,119,0,0,0,32.4,0.141,24,1
 2,112,86,42,160,38.4,0.246,28,0
 2,92,76,20,0,24.2,1.698,28,0
 6,183,94,0,0,40.8,1.461,45,0
 0,94,70,27,115,43.5,0.347,21,0
 2,108,64,0,0,30.8,0.158,21,0
 4,90,88,47,54,37.7,0.362,29,0
 0,125,68,0,0,24.7,0.206,21,0
 0,132,78,0,0,32.4,0.393,21,0
 5,128,80,0,0,34.6,0.144,45,0
 4,94,65,22,0,24.7,0.148,21,0
 7,114,64,0,0,27.4,0.732,34,1
 0,102,78,40,90,34.5,0.238,24,0
 2,111,60,0,0,26.2,0.343,23,0
 1,128,82,17,183,27.5,0.115,22,0
 10,92,62,0,0,25.9,0.167,31,0
 13,104,72,0,0,31.2,0.465,38,1
 5,104,74,0,0,28.8,0.153,48,0
 2,94,76,18,66,31.6,0.649,23,0
 7,97,76,32,91,40.9,0.871,32,1
 1,100,74,12,46,19.5,0.149,28,0
 0,102,86,17,105,29.3,0.695,27,0
 4,128,70,0,0,34.3,0.303,24,0
 6,147,80,0,0,29.5,0.178,50,1
 4,90,0,0,0,28,0.61,31,0
 3,103,72,30,152,27.6,0.73,27,0
 2,157,74,35,440,39.4,0.134,30,0
 1,167,74,17,144,23.4,0.447,33,1
 0,179,50,36,159,37.8,0.455,22,1
 11,136,84,35,130,28.3,0.26,42,1
 0,107,60,25,0,26.4,0.133,23,0
 1,91,54,25,100,25.2,0.234,23,0
 1,117,60,23,106,33.8,0.466,27,0
 5,123,74,40,77,34.1,0.269,28,0
 2,120,54,0,0,26.8,0.455,27,0
 1,106,70,28,135,34.2,0.142,22,0
 2,155,52,27,540,38.7,0.24,25,1
 2,101,58,35,90,21.8,0.155,22,0
 1,120,80,48,200,38.9,1.162,41,0
 11,127,106,0,0,39,0.19,51,0
 3,80,82,31,70,34.2,1.292,27,1
 10,162,84,0,0,27.7,0.182,54,0
 1,199,76,43,0,42.9,1.394,22,1
 8,167,106,46,231,37.6,0.165,43,1
 9,145,80,46,130,37.9,0.637,40,1
 6,115,60,39,0,33.7,0.245,40,1
 1,112,80,45,132,34.8,0.217,24,0
 4,145,82,18,0,32.5,0.235,70,1
 10,111,70,27,0,27.5,0.141,40,1
 6,98,58,33,190,34,0.43,43,0
 9,154,78,30,100,30.9,0.164,45,0
 6,165,68,26,168,33.6,0.631,49,0
 1,99,58,10,0,25.4,0.551,21,0
 10,68,106,23,49,35.5,0.285,47,0
 3,123,100,35,240,57.3,0.88,22,0
 8,91,82,0,0,35.6,0.587,68,0
 6,195,70,0,0,30.9,0.328,31,1
 9,156,86,0,0,24.8,0.23,53,1
 0,93,60,0,0,35.3,0.263,25,0
 3,121,52,0,0,36,0.127,25,1
 2,101,58,17,265,24.2,0.614,23,0
 2,56,56,28,45,24.2,0.332,22,0
 0,162,76,36,0,49.6,0.364,26,1
 0,95,64,39,105,44.6,0.366,22,0
 4,125,80,0,0,32.3,0.536,27,1
 5,136,82,0,0,0,0.64,69,0
 2,129,74,26,205,33.2,0.591,25,0
 3,130,64,0,0,23.1,0.314,22,0
 1,107,50,19,0,28.3,0.181,29,0
 1,140,74,26,180,24.1,0.828,23,0
 1,144,82,46,180,46.1,0.335,46,1
 8,107,80,0,0,24.6,0.856,34,0
 13,158,114,0,0,42.3,0.257,44,1
 2,121,70,32,95,39.1,0.886,23,0
 7,129,68,49,125,38.5,0.439,43,1
 2,90,60,0,0,23.5,0.191,25,0
 7,142,90,24,480,30.4,0.128,43,1
 3,169,74,19,125,29.9,0.268,31,1
 0,99,0,0,0,25,0.253,22,0
 4,127,88,11,155,34.5,0.598,28,0
 4,118,70,0,0,44.5,0.904,26,0
 2,122,76,27,200,35.9,0.483,26,0
 6,125,78,31,0,27.6,0.565,49,1
 1,168,88,29,0,35,0.905,52,1
 2,129,0,0,0,38.5,0.304,41,0
 4,110,76,20,100,28.4,0.118,27,0
 6,80,80,36,0,39.8,0.177,28,0
 10,115,0,0,0,0,0.261,30,1
 2,127,46,21,335,34.4,0.176,22,0
 9,164,78,0,0,32.8,0.148,45,1
 2,93,64,32,160,38,0.674,23,1
 3,158,64,13,387,31.2,0.295,24,0
 5,126,78,27,22,29.6,0.439,40,0
 10,129,62,36,0,41.2,0.441,38,1
 0,134,58,20,291,26.4,0.352,21,0
 3,102,74,0,0,29.5,0.121,32,0
 7,187,50,33,392,33.9,0.826,34,1
 3,173,78,39,185,33.8,0.97,31,1
 10,94,72,18,0,23.1,0.595,56,0
 1,108,60,46,178,35.5,0.415,24,0
 5,97,76,27,0,35.6,0.378,52,1
 4,83,86,19,0,29.3,0.317,34,0
 1,114,66,36,200,38.1,0.289,21,0
 1,149,68,29,127,29.3,0.349,42,1
 5,117,86,30,105,39.1,0.251,42,0
 1,111,94,0,0,32.8,0.265,45,0
 4,112,78,40,0,39.4,0.236,38,0
 1,116,78,29,180,36.1,0.496,25,0
 0,141,84,26,0,32.4,0.433,22,0
 2,175,88,0,0,22.9,0.326,22,0
 2,92,52,0,0,30.1,0.141,22,0
 3,130,78,23,79,28.4,0.323,34,1
 8,120,86,0,0,28.4,0.259,22,1
 2,174,88,37,120,44.5,0.646,24,1
 2,106,56,27,165,29,0.426,22,0
 2,105,75,0,0,23.3,0.56,53,0
 4,95,60,32,0,35.4,0.284,28,0
 0,126,86,27,120,27.4,0.515,21,0
 8,65,72,23,0,32,0.6,42,0
 2,99,60,17,160,36.6,0.453,21,0
 1,102,74,0,0,39.5,0.293,42,1
 11,120,80,37,150,42.3,0.785,48,1
 3,102,44,20,94,30.8,0.4,26,0
 1,109,58,18,116,28.5,0.219,22,0
 9,140,94,0,0,32.7,0.734,45,1
 13,153,88,37,140,40.6,1.174,39,0
 12,100,84,33,105,30,0.488,46,0
 1,147,94,41,0,49.3,0.358,27,1
 1,81,74,41,57,46.3,1.096,32,0
 3,187,70,22,200,36.4,0.408,36,1
 6,162,62,0,0,24.3,0.178,50,1
 4,136,70,0,0,31.2,1.182,22,1
 1,121,78,39,74,39,0.261,28,0
 3,108,62,24,0,26,0.223,25,0
 0,181,88,44,510,43.3,0.222,26,1
 8,154,78,32,0,32.4,0.443,45,1
 1,128,88,39,110,36.5,1.057,37,1
 7,137,90,41,0,32,0.391,39,0
 0,123,72,0,0,36.3,0.258,52,1
 1,106,76,0,0,37.5,0.197,26,0
 6,190,92,0,0,35.5,0.278,66,1
 2,88,58,26,16,28.4,0.766,22,0
 9,170,74,31,0,44,0.403,43,1
 9,89,62,0,0,22.5,0.142,33,0
 10,101,76,48,180,32.9,0.171,63,0
 2,122,70,27,0,36.8,0.34,27,0
 5,121,72,23,112,26.2,0.245,30,0
 1,126,60,0,0,30.1,0.349,47,1
 1,93,70,31,0,30.4,0.315,23,0
--- a/Tuning/diabetes.py
+++ b/Tuning/diabetes.py
@@ -0,0 +1,167 @@
 # Import our libraries
 import pandas as pd
 import numpy as np
 from sklearn.datasets import load_diabetes
 from sklearn.model_selection import train_test_split, RandomizedSearchCV
 from sklearn.model_selection import GridSearchCV
 from sklearn.metrics import accuracy_score, precision_score, recall_score
 from sklearn.metrics import f1_score
 from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier
 import matplotlib.pyplot as plt
 from sklearn.svm import SVC
 import seaborn as sns
 import sys
 import os
 sys.path.append(os.getcwd())
 import check_file as ch
 import warnings
 warnings.filterwarnings("ignore")
 sns.set(style="ticks")
 pd.set_option('display.max_columns', None)
 pd.set_option('display.max_colwidth', -1)
 # Read in our dataset
 diabetes = pd.read_csv('diabetes.csv')
 # Take a look at the first few rows of the dataset
 print(diabetes.head())
 # Summary on diabetes df
 print(diabetes.describe())
 # sns.pairplot(diabetes, hue='Outcome', diag_kind='hist')
 # plt.show()
 sns.heatmap(diabetes.corr(), annot=True, square=True,
            annot_kws={"size": 12})
 # plt.tight_layout()
 plt.show()
 diabetes.hist()
 plt.show()
 preg = diabetes.groupby(['Pregnancies', 'Outcome'])\
    .agg({'Pregnancies': 'count'})
 preg = preg.groupby(level=0).apply(lambda x: 100 * x / float(x.sum()))
 # Set our testing and training data
 y = diabetes['Outcome']
 X = diabetes.drop(['Outcome'], axis=1)
 AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None,
          learning_rate=0.1, n_estimators=200, random_state=None)
 X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size=0.2,
                                                    random_state=42)
 # build a classifier
 clf_rf = RandomForestClassifier()
 # Set up the hyperparameter search
 param_dist = {"max_depth": [3, None],
              "n_estimators": list(range(10, 200)),
              "max_features": list(range(1, X_test.shape[1] + 1)),
              "min_samples_split": list(range(2, 11)),
              "min_samples_leaf": list(range(1, 11)),
              "bootstrap": [True, False],
              "criterion": ["gini", "entropy"]}
 # Run a randomized search over the hyperparameters
 random_search = RandomizedSearchCV(clf_rf, param_distributions=param_dist)
 # Fit the model on the training data
 random_search.fit(X_train, y_train)
 # Make predictions on the test data
 rf_preds = random_search.best_estimator_.predict(X_test)
 ch.print_metrics(y_test, rf_preds, 'random forest')
 # Print the parameters used in the model
 print(random_search.best_estimator_)
 # build a classifier for ada boost
 clf_ada = AdaBoostClassifier()
 # Set up the hyperparameter search
 # look at  setting up your search for n_estimators, learning_rate
 # http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
 param_dist = {"n_estimators": [10, 100, 200, 400],
              "learning_rate": [0.001, 0.005, .01, 0.05, 0.1, 0.2, 0.3, 0.4,
                                0.5, 1, 2, 10, 20]}
 # Run a randomized search over the hyperparameters
 ada_search = RandomizedSearchCV(clf_ada, param_distributions=param_dist)
 # Fit the model on the training data
 ada_search.fit(X_train, y_train)
 # Make predictions on the test data
 ada_preds = ada_search.best_estimator_.predict(X_test)
 # Return your metrics on test data
 ch.print_metrics(y_test, ada_preds, 'adaboost')
 # Print the hyperparams used
 print(ada_search.best_estimator_)
 # Doing the same as above except using a full GridSearch
 ada_grid_search = GridSearchCV(clf_ada, param_dist)
 ada_grid_search.fit(X_train, y_train)
 ada_grid_preds = ada_grid_search.best_estimator_.predict(X_test)
 ch.print_metrics(y_test, ada_grid_preds, 'adaboost')
 print(ada_grid_search.best_estimator_)
 # build a classifier for support vector machines
 clf_svc = SVC()
 # Set up the hyperparameter search
 # look at setting up your search for C (recommend 0-10 range),
 # kernel, and degree
 # http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
 param_dist = {"C": [0.1, 0.5, 1, 3, 5],
              "kernel": ['linear', 'rbf']
              }
 # Run a randomized search over the hyperparameters
 svc_search = RandomizedSearchCV(clf_svc, param_distributions=param_dist)
 # Fit the model on the training data
 svc_search.fit(X_train, y_train)
 # Make predictions on the test data
 svc_preds = svc_search.best_estimator_.predict(X_test)
 ch.print_metrics(y_test, svc_preds, 'svc')
 print(svc_search.best_estimator_)
 # Get information about the best model
 # Get column names
 features = diabetes.columns[:diabetes.shape[1]]
 print(features)
 # Get the best features of the best estimator (random forest)
 importances = ada_search.best_estimator_.feature_importances_
 print(importances)
 # Get these in increasing number of importance
 indices = np.argsort(importances)
 print(indices)
 # Plot them
 plt.title('Feature Importances')
 plt.barh(range(len(indices)), importances[indices], color='b', align='center')
 plt.yticks(range(len(indices)), features[indices])
 plt.xlabel('Relative Importance')
 plt.show()
--- a/Tuning/grid_search.py
+++ b/Tuning/grid_search.py
@@ -0,0 +1,138 @@
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.model_selection import train_test_split
 from sklearn.metrics import f1_score, make_scorer
 import random
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.metrics import make_scorer
 from sklearn.model_selection import GridSearchCV
 # Fixing a random seed
 random.seed(42)
 def load_pts(csv_name):
    data = np.asarray(pd.read_csv(csv_name, header=None))
    X = data[:, 0:2]
    y = data[:, 2]
    plt.scatter(X[np.argwhere(y == 0).flatten(), 0], X[np.argwhere(
        y == 0).flatten(), 1], s=50, color='blue', edgecolor='k')
    plt.scatter(X[np.argwhere(y == 1).flatten(), 0], X[np.argwhere(
        y == 1).flatten(), 1], s=50, color='red', edgecolor='k')
    plt.xlim(-2.05, 2.05)
    plt.ylim(-2.05, 2.05)
    plt.grid(False)
    plt.tick_params(
        axis='x',
        which='both',
        bottom='off',
        top='off')
    return X, y
 X, y = load_pts('data_grid.csv')
 plt.show()
 # Split the data into training and testing sets
 X_train, X_test, y_train, y_test = train_test_split(X, y,
                                                    test_size=0.2,
                                                    random_state=42)
 # Define the model (with default hyperparameters)
 clf = DecisionTreeClassifier(random_state=42)
 # Fit the model
 clf.fit(X_train, y_train)
 # Make predictions
 train_predictions = clf.predict(X_train)
 test_predictions = clf.predict(X_test)
 # Now let's plot the model, and find the testing f1_score, to see how we did.
 def plot_model(X, y, clf):
    plt.scatter(X[np.argwhere(y == 0).flatten(), 0], X[np.argwhere(
        y == 0).flatten(), 1], s=50, color='blue', edgecolor='k')
    plt.scatter(X[np.argwhere(y == 1).flatten(), 0], X[np.argwhere(
        y == 1).flatten(), 1], s=50, color='red', edgecolor='k')
    plt.xlim(-2.05, 2.05)
    plt.ylim(-2.05, 2.05)
    plt.grid(False)
    plt.tick_params(
        axis='x',
        which='both',
        bottom='off',
        top='off')
    r = np.linspace(-2.1, 2.1, 300)
    s, t = np.meshgrid(r, r)
    s = np.reshape(s, (np.size(s), 1))
    t = np.reshape(t, (np.size(t), 1))
    h = np.concatenate((s, t), 1)
    z = clf.predict(h)
    s = s.reshape((np.size(r), np.size(r)))
    t = t.reshape((np.size(r), np.size(r)))
    z = z.reshape((np.size(r), np.size(r)))
    plt.contourf(s, t, z, colors=['blue', 'red'],
                 alpha=0.2, levels=range(-1, 2))
    if len(np.unique(z)) > 1:
        plt.contour(s, t, z, colors='k', linewidths=2)
    plt.legend(loc="best")
    plt.show()
 plot_model(X, y, clf)
 print('The Training F1 Score is', f1_score(train_predictions, y_train))
 print('The Testing F1 Score is', f1_score(test_predictions, y_test))
 # TODO: Create the parameters list you wish to tune.
 parameters = {'max_depth': [2, 4, 6, 8, 10],
              'min_samples_leaf': [2, 4, 6, 8, 10],
              'min_samples_split': [2, 4, 6, 8, 10]}
 # TODO: Make an fbeta_score scoring object.
 scorer = make_scorer(f1_score)
 # TODO: Perform grid search on the classifier using 'scorer' as the scoring
 # method.
 grid_obj = GridSearchCV(clf, parameters, scoring=scorer)
 # TODO: Fit the grid search object to the training data and find the optimal
 # parameters.
 grid_fit = grid_obj.fit(X_train, y_train)
 # Get the estimator.
 best_clf = grid_fit.best_estimator_
 # Fit the new model.
 best_clf.fit(X_train, y_train)
 # Make predictions using the new model.
 best_train_predictions = best_clf.predict(X_train)
 best_test_predictions = best_clf.predict(X_test)
 # Calculate the f1_score of the new model.
 print('The training F1 Score is', f1_score(best_train_predictions, y_train))
 print('The testing F1 Score is', f1_score(best_test_predictions, y_test))
 # Let's also explore what parameters ended up being used in the new model.
 print(best_clf)
 # Plot the new model.
 plot_model(X, y, best_clf)
--- a/Learning/Training
+++ b/Learning/Training
@@ -0,0 +1,35 @@
 from sklearn.model_selection import learning_curve
 # It is good to randomize the data before drawing Learning Curves
 def randomize(X, Y):
    permutation = np.random.permutation(Y.shape[0])
    X2 = X[permutation,:]
    Y2 = Y[permutation]
    return X2, Y2
 X2, y2 = randomize(X, y)
 def draw_learning_curves(X, y, estimator, num_trainings):
    train_sizes, train_scores, test_scores = learning_curve(
        estimator, X2, y2, cv=None, n_jobs=1, train_sizes=np.linspace(.1, 1.0, num_trainings))
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)
    plt.grid()
    plt.title("Learning Curves")
    plt.xlabel("Training examples")
    plt.ylabel("Score")
    plt.plot(train_scores_mean, 'o-', color="g",
             label="Training score")
    plt.plot(test_scores_mean, 'o-', color="y",
             label="Cross-validation score")
    plt.legend(loc="best")
    plt.show()