Understanding Machine Learning with Pythonを参考
丸写しなのでぶっちゃけ何もわかってない
Jupyter Notebook使用
別途データが必要
# coding: utf-8

# # Predicting Diabetes

# ## Import Libraries

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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np

get_ipython().magic('matplotlib inline')


# ## Loading and review data

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df = pd.read_csv("Notebooks/data/pima-data.csv")


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df.shape


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df.head(5)


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df.tail(5)


#    ## Check for null values

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df.isnull().values.any()


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def plot_corr(df, size=11):
    corr = df.corr()
    fig, ax = plt.subplots(figsize=(size, size))
    ax.matshow(corr)
    plt.xticks(range(len(corr.columns)), corr.columns)
    plt.yticks(range(len(corr.columns)), corr.columns)


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plot_corr(df)


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df.corr()


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df.head()


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del df['skin']


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df.head()


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plot_corr(df)


# ## Check Data Types

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df.head(5)


# Change True to 1, False to 0

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diabetes_map = {True : 1, False : 0}


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df['diabetes'] = df['diabetes'].map(diabetes_map)


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df.head(5)


# ## Check true/false ratio

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num_true = len(df.loc[df['diabetes'] == True])
num_false = len(df.loc[df['diabetes'] == False])
print("Number of True cases: {0} ({1:2.2f}%)".format(num_true, (num_true/ (num_true + num_false)) * 100))
print("Number of False cases: {0} ({1:2.2f}%)".format(num_false, (num_false/ (num_true + num_false)) * 100))


# ## Spliting the data
# 70% for training 30% for testing

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from sklearn.cross_validation import train_test_split

feature_col_names = ['num_preg', 'glucose_conc', 'diastolic_bp', 'thickness', 'insulin', 'bmi', 'diab_pred', 'age']
predicted_class_names = ['diabetes']

x = df[feature_col_names].values
y = df[predicted_class_names].values
split_test_size = 0.30

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=split_test_size, random_state=42)


# We check to ensure we have the desired 70% train, 30% test split of the data

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print("{0:0.2f}% in training set".format((len(x_train)/len(df.index)) * 100))
print("{0:0.2f}% in test set".format((len(x_test)/len(df.index)) * 100))


# #### Verifying predicted value was split correctly

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print("Original True     : {0} ({1:0.2f}%)".format(len(df.loc[df['diabetes'] == 1]), (len(df.loc[df['diabetes'] == 1])/len(df.index)) * 100))
print("Original False    : {0} ({1:0.2f}%)".format(len(df.loc[df['diabetes'] == 0]), (len(df.loc[df['diabetes'] == 0])/len(df.index)) * 100))
print("")
print("Training True    : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 1]), (len(y_train[y_train[:] == 1])/len(y_train)) * 100))
print("Training False   : {0} ({1:0.2f}%)".format(len(y_train[y_train[:] == 0]), (len(y_train[y_train[:] == 0])/len(y_train)) * 100))
print("")
print("Test True          : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 1]), (len(y_test[y_test[:] == 1])/len(y_test)) * 100))
print("Test False         : {0} ({1:0.2f}%)".format(len(y_test[y_test[:] == 0]), (len(y_test[y_test[:] == 0])/len(y_test)) * 100))
print("")


# ### Post-split Data Preparation

# #### Hidden Nissing Values

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df.head()


# #### Impute with the mean

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from sklearn.preprocessing import Imputer

fill_0 = Imputer(missing_values=0, strategy="mean", axis=0)

x_train = fill_0.fit_transform(x_train)
x_test = fill_0.fit_transform(x_test)


# ## Training Initial Algorithm - Naive Bayes

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from sklearn.naive_bayes import GaussianNB

nb_model = GaussianNB()

nb_model.fit(x_train, y_train.ravel())


# ### Performance on Training Data

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nb_predict_train = nb_model.predict(x_train)

from sklearn import metrics

print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_train, nb_predict_train)))
print()


# #### Metrics

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nb_predict_test = nb_model.predict(x_test)

from sklearn import metrics

print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_test, nb_predict_test)))
print()


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print("Confusion Matrix")

print("{0}".format(metrics.confusion_matrix(y_test, nb_predict_test, labels=[1, 0])))
print("")

print("Classification Report")
print(metrics.classification_report(y_test, nb_predict_test, labels=[1, 0]))


# ## Random Forest

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from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier(random_state=42)
rf_model.fit(x_train, y_train.ravel())


# ### Predict Training Data

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rf_predict_train = rf_model.predict(x_train)
print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_train, rf_predict_train)))


# ### Predict Test Data

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rf_predict_test = rf_model.predict(x_test)
print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_test, rf_predict_test)))


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print(metrics.confusion_matrix(y_test, rf_predict_test, labels=[1, 0]))
print("")
print("Classification Report")
print(metrics.classification_report(y_test, rf_predict_test, labels=[1, 0]))


# ### Logistic Regression

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from sklearn.linear_model import LogisticRegression

lr_model = LogisticRegression(C=0.7, random_state=42)
lr_model.fit(x_train, y_train.ravel())
lr_predict_test = lr_model.predict(x_test)

print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_test, lr_predict_test)))
print(metrics.confusion_matrix(y_test, lr_predict_test, labels=[1, 0]))
print("")
print("Classification Report")
print(metrics.classification_report(y_test, lr_predict_test, labels=[1, 0]))


# Setting regularization parameter

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C_start = 0.1
C_end = 5
C_inc = 0.1

C_values, recall_scores = [], []

C_val = C_start
best_recall_score = 0
while(C_val < C_end):
    C_values.append(C_val)
    lr_model_loop = LogisticRegression(C=C_val, random_state=42)
    lr_model_loop.fit(x_train, y_train.ravel())
    lr_predict_loop_test = lr_model_loop.predict(x_test)
    recall_score = metrics.recall_score(y_test, lr_predict_loop_test)
    recall_scores.append(recall_score)
    if(recall_score > best_recall_score):
        best_recall_score = recall_score
        best_lr_predict_test = lr_predict_loop_test
        
    C_val = C_val +C_inc

best_score_C_val = C_values[recall_scores.index(best_recall_score)]
print("1st max value of {0:.3f} occured at C={1:.3f}".format(best_recall_score, best_score_C_val))


get_ipython().magic('matplotlib inline')
plt.plot(C_values, recall_scores, "-")
plt.xlabel("C value")
plt.ylabel("recall score")


# ### Logistic regression with class_weight='balanced'

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C_start = 0.1
C_end = 5
C_inc = 0.1

C_values, recall_scores = [], []

C_val = C_start
best_recall_score = 0
while(C_val < C_end):
    C_values.append(C_val)
    lr_model_loop = LogisticRegression(C=C_val, class_weight="balanced", random_state=42)
    lr_model_loop.fit(x_train, y_train.ravel())
    lr_predict_loop_test = lr_model_loop.predict(x_test)
    recall_score = metrics.recall_score(y_test, lr_predict_loop_test)
    recall_scores.append(recall_score)
    if(recall_score > best_recall_score):
        best_recall_score = recall_score
        best_lr_predict_test = lr_predict_loop_test
        
    C_val = C_val +C_inc

best_score_C_val = C_values[recall_scores.index(best_recall_score)]
print("1st max value of {0:.3f} occured at C={1:.3f}".format(best_recall_score, best_score_C_val))


get_ipython().magic('matplotlib inline')
plt.plot(C_values, recall_scores, "-")
plt.xlabel("C value")
plt.ylabel("recall score")


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from sklearn.linear_model import LogisticRegression
lr_model = LogisticRegression(class_weight="balanced", C=best_score_C_val, random_state=42)
lr_model.fit(x_train, y_train.ravel())
lr_predict_test = lr_model.predict(x_test)

print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_test, lr_predict_test)))
print(metrics.confusion_matrix(y_test, lr_predict_test, labels=[1, 0]))
print("")
print("Classification Report")
print(metrics.classification_report(y_test, lr_predict_test, labels=[1, 0]))
print(metrics.recall_score(y_test, lr_predict_test))


# ### Logistic RegressionCV

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from sklearn.linear_model import LogisticRegressionCV
lr_cv_model = LogisticRegressionCV(n_jobs=-1, random_state=42, Cs=3, cv=10, refit=True, class_weight="balanced")
lr_cv_model.fit(x_train, y_train.ravel())


# ### Predict on Test data

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lr_cv_predict_test = lr_cv_model.predict(x_test)

print("Accuracy: {0:.4f}".format(metrics.accuracy_score(y_test, lr_cv_predict_test)))
print(metrics.confusion_matrix(y_test, lr_cv_predict_test, labels=[1, 0]))
print("")
print("Classification Report")
print(metrics.classification_report(y_test, lr_cv_predict_test, labels=[1, 0]))


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