python1/phuonganh.py at master · tamcb2708/python1 · GitHub

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import os

import matplotlib.pyplot as plt
import pandas as pd
from sklearn import linear_model
from sklearn.linear_model import Ridge
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures

def get_home_data():
    """Get home data, from local csv."""
    if os.path.exists("data/covid1.csv"):
        print("-- covid.csv found locally")
        df = pd.read_csv("data/covid1.csv")

    return df
df = get_home_data()
df.head()
df_house = df.sample(frac=1)
print (df_house.shape)
print (df_house.head(10))


def plotting_features_vs_target(features, x, y):
    # define number of subplot
    num_feature = len(features)
    f, axes = plt.subplots(1, num_feature, sharey=True)

    # plotting
    for i in range(0, num_feature):
        axes[i].scatter(x[features[i]], y)
        axes[i].set_title(features[i])

    plt.show()


if __name__ == "__main__":
    df = get_home_data()
    # features selection
    features = list(["county_name", "fips_code"])
    print("Features name:", list(df.columns.values))
    print("Selected features:", features)
    y = df["deaths"]
    X = df[features]

    # split data-set into training (70%) and testing set (30%)
    x_train, x_test, y_train, y_test = train_test_split(X, y)

    # plotting features, target relationships
    plotting_features_vs_target(features, x_train, y_train)


    # # training model
    # linear = linear_model.LinearRegression()
    # linear.fit(x_train, y_train)

    # # evaluating model
    # score_trained = linear.score(x_test, y_test)
    # print ("Model scored:", score_trained)


    # # L1 regularization
    # lasso_linear = linear_model.Lasso(alpha=1.0)
    # lasso_linear.fit(x_train, y_train)

    # # evaluating L1 regularized model
    # score_lasso_trained = lasso_linear.score(x_test, y_test)
    # print ("Lasso model scored:", score_lasso_trained)


    # # L2 regularization
    # ridge_linear = Ridge(alpha=1.0)
    # ridge_linear.fit(x_train, y_train)

    # # evaluating L2 regularized model
    # score_ridge_trained = ridge_linear.score(x_test, y_test)
    # print ("Ridge model scored:", score_ridge_trained)


    # poly_model = Pipeline([('poly', PolynomialFeatures(degree=2)),
    #                        ('linear', linear_model.LinearRegression(fit_intercept=False))])
    # poly_model = poly_model.fit(x_train, y_train)
    # score_poly_trained = poly_model.score(x_test, y_test)
    # print ("Poly model scored:", score_poly_trained)

    # poly_model = Pipeline([('poly', PolynomialFeatures(interaction_only=True, degree=2)),
    #                        ('linear', linear_model.LinearRegression(fit_intercept=False))])
    # poly_model = poly_model.fit(x_train, y_train)
    # score_poly_trained = poly_model.score(x_test, y_test)
    # print ("Poly model (interaction only) scored:", score_poly_trained)