%matplotlib inline

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import time

sns.set_context("talk")

import numpy as np
from sklearn.linear_model import LinearRegression

# Training output samples
height_inches = np.array([[51.0, 56.0, 64.0, 71.0, 69.0]]).T

# Training feature samples
age_years = np.array([[7.8, 10.7, 13.7, 17.5, 20.1]]).T

# Initialize model
model = LinearRegression()

# Train
model.fit(age_years, height_inches)

# Predict
model.predict(15.0)

def plot_boys(age, height, test=None, pred=None):
    plt.plot(age_years, height_inches, marker='o', ls='none')
    plt.xlabel("Age (years)")
    plt.ylabel("Height (inches)")
    plt.title("Boys")
    if test is not None:
        plt.plot(test, pred, '-');

plot_boys(age_years, height_inches);

test = np.array([np.linspace(7.0, 21.0)]).T
pred = model.predict(test)

plot_boys(age_years, height_inches, test, pred)

test = np.array([np.linspace(0.0, 50.0)]).T
pred = model.predict(test)

plot_boys(age_years, height_inches, test, pred)

from sklearn.preprocessing import PolynomialFeatures

pf = PolynomialFeatures(4)
x_poly = pf.fit_transform(age_years)

model.fit(x_poly, height_inches)

test = np.array([np.linspace(7.0, 21.0)]).T
test_poly = pf.transform(test)
pred = model.predict(test_poly)

plot_boys(age_years, height_inches, test, pred)

test = np.array([np.linspace(0.0, 50.0)]).T
test_poly = pf.transform(test)
pred = model.predict(test_poly)

plot_boys(age_years, height_inches, test, pred)

import csv
dtypes = {
    "PassengerId":np.int64,
    "Survived":object,
    "Pclass":np.int64,
    "Name":object,
    "Sex":object,
    "Age":np.float64,
}

train_df = pd.read_csv("train.csv", dtype=dtypes)
train_df

import googleprediction

model = googleprediction.GooglePredictor(
    "myproject",
    "mybucket/train_cleaned.csv",
    "hastalapasta",
    "client_secrets.json")

def survived(pred):
    pred = pred[0]
    if pred == u'1':
        print "YES"
    else:
        print "NO"

pred = model.predict([[
    '1',  # Fare class
    'Spencer Mrs William Augustus Marie Eugenie',  # Name
    'female',  # Gender
    20.2,  # Age
    1,  # Number of parents or children aboard
    0,  # Number of siblings or spouse aboard
    146.5208,  # Fare price
    ],])

survived(pred) 

pred = model.predict([[
    '1',  # Fare class
    'Frank Lampard',  # Name
    'male',  # Gender
    36.0,  # Age
    0,  # Number of parents or children aboard
    0,  # Number of siblings or spouse aboard
    20.0,  # Fare price
    ],])
survived(pred) 

pred = model.predict([[
    '1',  # Fare class
    'Frank Lampard',  # Name
    'male',  # Gender
    36.0,  # Age
    0,  # Number of parents or children aboard
    0,  # Number of siblings or spouse aboard
    500.0,  # Fare price
    ],])
survived(pred)