import pandas as pd
import numpy as np
import functools

GENDER = "Gender (1 =F, 0=M)"
TOY_STORY = "1: Toy Story (1995)"

def dbg(v):
    print(v)
    return v

def correl(x, y):
    x = x.dropna()
    y = y.dropna()
    xb = x.mean()
    yb = y.mean()
    num = sum(map(lambda c: (c[0] - xb) * (c[1] - yb), zip(x, y)))
    den = np.sqrt(sum(map(lambda x: pow(x - xb, 2), x)) *
            sum(map(lambda y: pow(y - yb, 2), y)))
    return num / den
X = pd.Series([195, 151, 148, 189, 183, 154])
Y = pd.Series([200, 180, 178, 165, 192, 144])
print(correl(X, Y))
assert np.isclose(correl(X, Y), 0.46706598573232)

data = pd.read_csv("HW1-data.csv")
gender_data = data.iloc[:, :2].to_dict(orient="list")
users = gender_data["User"]
gender_data = dict(zip(gender_data["User"], gender_data[GENDER]))
print(gender_data)
# print(gender_data)

movie_data = data.iloc[:, 2:]
# movie_data.columns = movie_data.iloc[0]
# print(movie_data)

print("Response 1: highest average")
print(movie_data.mean().sort_values(ascending=False)[:3])
print()

print("Response 2: popularity (# of reviews)")
print(movie_data.count().sort_values(ascending=False)[:3])
print()

print("Response 3: %reviews greater than 4")
print(movie_data.apply(lambda s: (s >= 4).sum() / s.count()).sort_values(ascending=False)[:3])
print()

print("Response 4: %correlation with Toy Story")
toy_story_raters = movie_data.transpose().loc[TOY_STORY]
toy_story_raters_n = toy_story_raters.notna()
def toy_story_correlate(s):
    xy = 0
    for i, v in s.notna().items():
        if toy_story_raters_n[i] and v:
            xy += 1
    x = toy_story_raters.count()
    return xy/x
print(movie_data.apply(toy_story_correlate).sort_values(ascending=False)[:5])
print()

print("Response 5: correlation with Toy Story")
def toy_story_correlate2(s):
    return correl(s, toy_story_raters)
print(movie_data.apply(toy_story_correlate2).sort_values(ascending=False))
print("TODO NOT DONE")
print()

print("Response 6: Mean difference by gender")
isman = lambda id: not gender_data[id]
iswoman = lambda id: gender_data[id]
def gender_rating(g, s):
    v = pd.Series(map(lambda c: c[1], filter(lambda c: gender_data[users[c[0]]] == g, s.items())))
    return v.mean()
men_ratings = movie_data.apply(functools.partial(gender_rating, 0))
women_ratings = movie_data.apply(functools.partial(gender_rating, 1))
print(men_ratings, women_ratings)