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python/Supervised Learning/Training and Tuning/diabetes.py

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+# 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()