In [107]:
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = “all”
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(“whitegrid”)
sns.set_context(“notebook”)
#sns.set_context(“poster”)
In [108]:
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn import metrics
from sklearn import preprocessing
Basic Regression Algorithms
Here we review seven of the most well-known regression algorithms.
Four variations of linear regression:
• Logistic Regression.
• Ridge Linear Regression.
• Lasso Linear Regression.
• Elastic Net Regression.
and three non-linear that we already know as classification algorithms:
• k-nn – k-Nearest Neighbors.
• CART – Classification and Regression Trees.
• SVM – Support Vector Machines.
Again we will compare the result and plot it, trying to address the question of which algorithm works better with this dataset.
For the dataset, we will use a well-known one that we previously used: the Boston Housing Price with a 10-fold cross-validation. Obviously in this case the objective is to approximate the pricing.
As a metric we will use the mean squarred error. Please note that scikit-learn follows a convention that imposes all metrics to be sorted in ascending order, therefore the larger is always better. In order to follow this convention, mean squarred error is expressed with a negative sign, so larger is better (in this case 0).


In [109]:
# Load the Boston dataset and separate input and output components
from numpy import set_printoptions
set_printoptions(precision=3)
filename=”HousingData.csv”
b_housing=pd.read_csv(filename)
b_housing.head()
b_housing.fillna(0,inplace=True) # we have NaN
# First we separate into input and output components
array=b_housing.values
X=array[:,0:13]
y=array[:,13]
np.set_printoptions(suppress=True)
X
pd.DataFrame(X).head()
# Create the DataFrames for plotting
resall=pd.DataFrame()
res_w1=pd.DataFrame()
Out[109]:
CRIM
ZN
INDUS
CHAS
NOX
RM
AGE
DIS
RAD
TAX
PTRATIO
B
LSTAT
MEDV
0
0.00632
18.0
2.31
0.0
0.538
6.575
65.2
4.0900
1
296
15.3
396.90
4.98
24.0
1
0.02731
0.0
7.07
0.0
0.469
6.421
78.9
4.9671
2
242
17.8
396.90
9.14
21.6
2
0.02729
0.0
7.07
0.0
0.469
7.185
61.1
4.9671
2
242
17.8
392.83
4.03
34.7
3
0.03237
0.0
2.18
0.0
0.458
6.998
45.8
6.0622
3
222
18.7
394.63
2.94
33.4
4
0.06905
0.0
2.18
0.0
0.458
7.147
54.2
6.0622
3
222
18.7
396.90
NaN
36.2
Out[109]:
array([[ 0.006, 18. , 2.31 , …, 15.3 , 396.9 , 4.98 ],
[ 0.027, 0. , 7.07 , …, 17.8 , 396.9 , 9.14 ],
[ 0.027, 0. , 7.07 , …, 17.8 , 392.83 , 4.03 ],
…,
[ 0.061, 0. , 11.93 , …, 21. , 396.9 , 5.64 ],
[ 0.11 , 0. , 11.93 , …, 21. , 393.45 , 6.48 ],
[ 0.047, 0. , 11.93 , …, 21. , 396.9 , 7.88 ]])
Out[109]:
0
1
2
3
4
5
6
7
8
9
10
11
12
0
0.00632
18.0
2.31
0.0
0.538
6.575
65.2
4.0900
1.0
296.0
15.3
396.90
4.98
1
0.02731
0.0
7.07
0.0
0.469
6.421
78.9
4.9671
2.0
242.0
17.8
396.90
9.14
2
0.02729
0.0
7.07
0.0
0.469
7.185
61.1
4.9671
2.0
242.0
17.8
392.83
4.03
3
0.03237
0.0
2.18
0.0
0.458
6.998
45.8
6.0622
3.0
222.0
18.7
394.63
2.94
4
0.06905
0.0
2.18
0.0
0.458
7.147
54.2
6.0622
3.0
222.0
18.7
396.90
0.00
Linear Regression¶
You all are probably familiar with linear regression!
Just a small reminder, it assumes a Gaussian distribution and that all variables are relevant. It also assumes that variables are not highly correlated (a problem called collinearity).
We will discuss both the statistical approach to Linear Regression and the Machine Learning approach. You will see that in the statistical approach we seek to fit a model to an existing set of data with the objective to find the components that explain this fit. In contrast, in machine learning we aim to build a model that is able to work well and predict with unknown sets of data.
We will use the LinearRegression class.
In [110]:
# Linear Regression as used in statistics and social science
# we use statsmodel
import statsmodels.api as sm
X_pd=b_housing.copy()
X_pd=X_pd.drop([“MEDV”],axis=1)
y_pd=b_housing[“MEDV”]
X_pd=sm.add_constant(X_pd)
model = sm.OLS(y_pd,X_pd).fit()
model.summary()
/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.
return ptp(axis=axis, out=out, **kwargs)
Out[110]:
OLS Regression Results
Dep. Variable:
MEDV
R-squared:
0.727
Model:
OLS
Adj. R-squared:
0.720
Method:
Least Squares
F-statistic:
100.7
Date:
Tue, 12 Nov 2019
Prob (F-statistic):
2.06e-129
Time:
01:37:01
Log-Likelihood:
-1511.9
No. Observations:
506
AIC:
3052.
Df Residuals:
492
BIC:
3111.
Df Model:
13
Covariance Type:
nonrobust
coef
std err
t
P>|t|
[0.025
0.975]
const
33.2336
5.204
6.387
0.000
23.009
43.458
CRIM
-0.1162
0.033
-3.547
0.000
-0.181
-0.052
ZN
0.0429
0.014
3.136
0.002
0.016
0.070
INDUS
-0.0315
0.052
-0.601
0.548
-0.134
0.071
CHAS
3.1311
0.892
3.510
0.000
1.378
4.884
NOX
-17.3454
3.727
-4.654
0.000
-24.669
-10.022
RM
4.3036
0.412
10.458
0.000
3.495
5.112
AGE
-0.0151
0.010
-1.494
0.136
-0.035
0.005
DIS
-1.4844
0.196
-7.592
0.000
-1.868
-1.100
RAD
0.2685
0.067
3.998
0.000
0.137
0.400
TAX
-0.0109
0.004
-2.874
0.004
-0.018
-0.003
PTRATIO
-0.9815
0.134
-7.348
0.000
-1.244
-0.719
B
0.0094
0.003
3.373
0.001
0.004
0.015
LSTAT
-0.3918
0.044
-8.817
0.000
-0.479
-0.304
Omnibus:
188.045
Durbin-Watson:
1.032
Prob(Omnibus):
0.000
Jarque-Bera (JB):
1006.627
Skew:
1.541
Prob(JB):
2.59e-219
Kurtosis:
9.184
Cond. No.
1.49e+04
Warnings:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.49e+04. This might indicate that there are
strong multicollinearity or other numerical problems.
In [111]:
# Linear Regression with scikit-learn
from sklearn.linear_model import LinearRegression
kfold=KFold(n_splits=10, random_state=7)
model=LinearRegression()
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’Linear Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”Lin”
resall=pd.concat([resall,res_w1], ignore_index=True)
# Now lets use it in the same way than the statsmodel
model_x=LinearRegression()
model_x.fit(X,y)
print(f’Intercept {model_x.intercept_:.4f}’)
print(“Coefficients “,model_x.coef_)
y_pred_x=model_x.predict(X)
print(f’MAE – Mean Absolute Error {metrics.mean_absolute_error(y, y_pred_x):.3f}’)
print(f’MSE – Mean Square Error {metrics.mean_squared_error(y, y_pred_x):.3f}’)
print(f’R2 {metrics.r2_score(y, y_pred_x):.3f}’)
Linear Regression – MSE -34.090 std 44.046
Out[111]:
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)
Intercept 33.2336
Coefficients [ -0.116 0.043 -0.032 3.131 -17.345 4.304 -0.015 -1.484 0.269
-0.011 -0.981 0.009 -0.392]
MAE – Mean Absolute Error 3.308
MSE – Mean Square Error 23.058
R2 0.727
Ridge Regression¶
The Ridge regession corresponds to modern linear regression algorithms that aim to be more robust to outliers.
In this case the loss function is modified to minimize the complexity of the model measured as the sum squared value of the coefficietn values (also called the L2-norm).
We will use the Ridge class.
In [112]:
# Ridge Regression
from sklearn.linear_model import Ridge
kfold=KFold(n_splits=10, random_state=7)
model=Ridge()
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’Ridge Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”Ridge”
resall=pd.concat([resall,res_w1], ignore_index=True)
Ridge Regression – MSE -33.384 std 44.258
Lasso Regression¶
Again is a modern regression algorithm that aims a reducing the weight of outliers.
In this case the loss function is modified measuring the complexity of the model as the sum absolute value of the coefficients values (also called the L1-norm).
You can construct a Lasso model using the Lasso class.
In [113]:
# Lasso Regression
from sklearn.linear_model import Lasso
kfold=KFold(n_splits=10, random_state=7)
model=Lasso()
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’Lasso Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”Lasso”
resall=pd.concat([resall,res_w1], ignore_index=True)
Lasso Regression – MSE -35.161 std 32.383
ElasticNet Regression¶
The ElasticNet Regression combines both the Ridge and the Lasso.
It penalizes the model using both the L1-norm and the L2-norm.
You can construct an ElasticNet model using the ElasticNet class.
In [114]:
# ElasticNet Regression
from sklearn.linear_model import ElasticNet
kfold=KFold(n_splits=10, random_state=7)
model=ElasticNet()
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’ElasticNet Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”ElasticNet”
resall=pd.concat([resall,res_w1], ignore_index=True)
ElasticNet Regression – MSE -33.109 std 26.963
k-nn k-Nearest Neighbors¶
k-Nearerst Neighbors is a non-linear machine learning algorithm that uses distance metrics to find the most similar k-elements, taking the mean or median outcome of the neighbors as the prediction.
One interesting advantage of this algorithm is that we can choose a different metric for calculating the distance. The default metric is Minkowski, equivalent to euclidean (with p=2). It can be easily transformed to Mnahattan distance with p=1.
For constructing a knn model for regression you must use the KNeighorsRegressor class.
In [115]:
# KNN Regression
from sklearn.neighbors import KNeighborsRegressor
kfold=KFold(n_splits=10, random_state=7)
model=KNeighborsRegressor()
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’KNN Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”KNN”
resall=pd.concat([resall,res_w1], ignore_index=True)
KNN Regression – MSE -97.175 std 50.572
CART – Classification and Regression Trees¶
Cart builds a binary tree from the data where the splits are chosen greedly evaluating all the attributes in order to minimize a cost function. The default cost metric for regression decision trees is the mean squared error, specified in the criterion parameter.
For CART we will use the DecisionTreeRegressor class.
In [116]:
# Decision Trees Regression
# Please observe that in this case repeating the algorithm gives different results
# scaling doesn’t matter in this case – you get different results but inside the range
from sklearn.tree import DecisionTreeRegressor
kfold=KFold(n_splits=10, random_state=7)
model=DecisionTreeRegressor()
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’Decision Trees Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”Trees”
resall=pd.concat([resall,res_w1], ignore_index=True)
Decision Trees Regression – MSE -38.452 std 30.760
Support Vector Machines¶
Support vector machines seeks a line that separates best two classes. The data instances that are closest to this line are, better separating the classes, are called support vectors.
Support Vector Machines have the advantage that you can change the kernel function to use. Radial basis function is used by default, a pretty powerful one.
You can construct a SVM model for regression with the SVR class.
In [117]:
# SVM – Support Vector Machines
from sklearn.svm import SVR
kfold=KFold(n_splits=10, random_state=7)
model=SVR(gamma=”auto”)
scoring = “neg_mean_squared_error”
results=cross_val_score(model, X, y, cv=kfold, scoring=scoring)
print(f’SVM Regression – MSE {results.mean():.3f} std {results.std():.3f}’)
res_w1[“Res”]=results
res_w1[“Type”]=”SVM”
resall=pd.concat([resall,res_w1], ignore_index=True)
SVM Regression – MSE -91.129 std 71.147
Algorithm Comparison¶
In [118]:
# Now let’s compare them all
plt.figure(figsize=(15,9))
sns.boxplot(data=resall, x=”Type”, y=”Res”)
sns.swarmplot(data=resall, x=”Type”, y=”Res”, color=”royalblue”)
Out[118]:
Out[118]:
Out[118]:

In [ ]:
In [ ]:
In [ ]:
Mission 1
a) Let’s predict movie revenue using the Movie Database in Kaggle https://www.kaggle.com/c/tmdb-box-office-prediction
In [4]:
import pandas as pd
pd.read_csv(“movie.csv”)
Out[4]:
id
belongs_to_collection
budget
genres
homepage
imdb_id
original_language
original_title
overview
popularity
…
production_countries
release_date
runtime
spoken_languages
status
tagline
title
Keywords
cast
crew
0
3001
[{‘id’: 34055, ‘name’: ‘Pokémon Collection’, ‘…
0
[{‘id’: 12, ‘name’: ‘Adventure’}, {‘id’: 16, ‘…
http://www.pokemon.com/us/movies/movie-pokemon…
tt1226251
ja
ディアルガVSパルキアVSダークライ
Ash and friends (this time accompanied by newc…
3.851534
…
[{‘iso_3166_1’: ‘JP’, ‘name’: ‘Japan’}, {‘iso_…
7/14/07
90.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}, {‘iso…
Released
Somewhere Between Time & Space… A Legend Is …
Pokémon: The Rise of Darkrai
[{‘id’: 11451, ‘name’: ‘pok√©mon’}, {‘id’: 115…
[{‘cast_id’: 3, ‘character’: ‘Tonio’, ‘credit_…
[{‘credit_id’: ’52fe44e7c3a368484e03d683′, ‘de…
1
3002
NaN
88000
[{‘id’: 27, ‘name’: ‘Horror’}, {‘id’: 878, ‘na…
NaN
tt0051380
en
Attack of the 50 Foot Woman
When an abused wife grows to giant size becaus…
3.559789
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
5/19/58
65.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}]
Released
A titanic beauty spreads a macabre wave of hor…
Attack of the 50 Foot Woman
[{‘id’: 9748, ‘name’: ‘revenge’}, {‘id’: 9951,…
[{‘cast_id’: 2, ‘character’: ‘Nancy Fowler Arc…
[{‘credit_id’: ‘55807805c3a3685b1300060b’, ‘de…
2
3003
NaN
0
[{‘id’: 35, ‘name’: ‘Comedy’}, {‘id’: 10749, ‘…
NaN
tt0118556
en
Addicted to Love
Good-natured astronomer Sam is devastated when…
8.085194
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
5/23/97
100.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}]
Released
A Comedy About Lost Loves And Last Laughs
Addicted to Love
[{‘id’: 931, ‘name’: ‘jealousy’}, {‘id’: 9673,…
[{‘cast_id’: 11, ‘character’: ‘Maggie’, ‘credi…
[{‘credit_id’: ’52fe4330c3a36847f8041367′, ‘de…
3
3004
NaN
6800000
[{‘id’: 18, ‘name’: ‘Drama’}, {‘id’: 10752, ‘n…
http://www.sonyclassics.com/incendies/
tt1255953
fr
Incendies
A mother’s last wishes send twins Jeanne and S…
8.596012
…
[{‘iso_3166_1’: ‘CA’, ‘name’: ‘Canada’}, {‘iso…
9/4/10
130.0
[{‘iso_639_1’: ‘fr’, ‘name’: ‘Français’}, {‘is…
Released
The search began at the opening of their mothe…
Incendies
[{‘id’: 378, ‘name’: ‘prison’}, {‘id’: 539, ‘n…
[{‘cast_id’: 6, ‘character’: ‘Nawal’, ‘credit_…
[{‘credit_id’: ‘56478092c3a36826140043af’, ‘de…
4
3005
NaN
2000000
[{‘id’: 36, ‘name’: ‘History’}, {‘id’: 99, ‘na…
NaN
tt0418753
en
Inside Deep Throat
In 1972, a seemingly typical shoestring budget…
3.217680
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
2/11/05
92.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}]
Released
It was filmed in 6 days for 25 thousand dollar…
Inside Deep Throat
[{‘id’: 279, ‘name’: ‘usa’}, {‘id’: 1228, ‘nam…
[{‘cast_id’: 1, ‘character’: ‘Narrator (voice)…
[{‘credit_id’: ’52fe44ce9251416c75041967′, ‘de…
5
3006
NaN
0
[{‘id’: 35, ‘name’: ‘Comedy’}, {‘id’: 18, ‘nam…
NaN
tt0120238
en
SubUrbia
A group of suburban teenagers try to support e…
8.679350
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
2/7/96
121.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}]
Released
NaN
SubUrbia
[{‘id’: 10183, ‘name’: ‘independent film’}]
[{‘cast_id’: 4, ‘character’: ‘Pony’, ‘credit_i…
[{‘credit_id’: ’52fe4576c3a368484e05c901′, ‘de…
6
3007
NaN
0
[{‘id’: 10749, ‘name’: ‘Romance’}, {‘id’: 18, …
NaN
tt1517177
de
Drei
Hanna and Simon are in a 20 year marriage with…
4.898882
…
[{‘iso_3166_1’: ‘DE’, ‘name’: ‘Germany’}]
12/23/10
119.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}, {‘iso…
Released
Imagine the possibilities.
Three
[{‘id’: 572, ‘name’: ‘sex’}, {‘id’: 154937, ‘n…
[{‘cast_id’: 2, ‘character’: ‘Hanna’, ‘credit_…
[{‘credit_id’: ’52fe485bc3a36847f816358d’, ‘de…
7
3008
NaN
30000000
[{‘id’: 16, ‘name’: ‘Animation’}, {‘id’: 10751…
http://www.tigger.com
tt0220099
en
The Tigger Movie
As it happens, everybody – Pooh, Piglet, Eeyor…
7.023414
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
2/11/00
77.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}]
Released
NaN
The Tigger Movie
[{‘id’: 3905, ‘name’: ‘owl’}, {‘id’: 4144, ‘na…
[{‘cast_id’: 2, ‘character’: ‘Tigger / Winnie …
[{‘credit_id’: ‘59121da0c3a3686519043247’, ‘de…
8
3009
NaN
16500000
[{‘id’: 18, ‘name’: ‘Drama’}, {‘id’: 10749, ‘n…
http://becomingjane-themovie.com/
tt0416508
en
Becoming Jane
A biographical portrait of a pre-fame Jane Aus…
7.829737
…
[{‘iso_3166_1’: ‘GB’, ‘name’: ‘United Kingdom’…
3/2/07
120.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}, {‘iso…
Released
Her own life is her greatest inspiration.
Becoming Jane
[{‘id’: 392, ‘name’: ‘england’}, {‘id’: 934, ‘…
[{‘cast_id’: 10, ‘character’: ‘Jane Austen’, ‘…
[{‘credit_id’: ‘53569575c3a3687f54000051’, ‘de…
9
3010
[{‘id’: 10194, ‘name’: ‘Toy Story Collection’,…
90000000
[{‘id’: 16, ‘name’: ‘Animation’}, {‘id’: 35, ‘…
http://toystory.disney.com/toy-story-2
tt0120363
en
Toy Story 2
Andy heads off to Cowboy Camp, leaving his toy…
17.547693
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
10/30/99
92.0
[{‘iso_639_1’: ‘en’, ‘name’: ‘English’}]
Released
The toys are back!
Toy Story 2
[{‘id’: 2598, ‘name’: ‘museum’}, {‘id’: 3246, …
[{‘cast_id’: 18, ‘character’: ‘Woody (voice)’,…
[{‘credit_id’: ’52fe4284c3a36847f8025073′, ‘de…
10
3011
NaN
2000000
[{‘id’: 53, ‘name’: ‘Thriller’}, {‘id’: 27, ‘n…
NaN
tt0419663
en
Cruel World
Millions witnessed Philip Markham’s public hum…
0.262437
…
[{‘iso_3166_1’: ‘US’, ‘name’: ‘United States o…
9/1/05
88.0
[{‘iso_639_1’: ‘ru’, ‘name’: ‘Pусский’}, {‘iso…
Released
When You Get Voted Off This Reality Show… It…
Cruel World
[{‘id’: 2136, ‘name’: ‘television’}, {‘id’: 30…
[{‘cast_id’: 1, ‘character’: ‘Philip Markham’,…
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Bande de filles
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Girlhood
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The Gods Must Be Crazy
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Raising Victor Vargas
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Raising Victor Vargas
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The Brothers Bloom
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They’d never let the truth come between them.
The Brothers Bloom
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Beautiful Boy
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Hot Tub Time Machine
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Kick some past
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Transcendence
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All That Jazz
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12/20/79
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All that work. All that glitter. All that pain…
All That Jazz
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Titanic
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194.0
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Nothing on Earth could come between them.
Titanic
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Very Bad Things
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They’ve been bad. Very bad.
Very Bad Things
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My Best Friend’s Girl
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It’s funny what love can make you do…
My Best Friend’s Girl
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Broken Bridges
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Broken Bridges
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Suspect Zero
A killer is on the loose, and an FBI agent sif…
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Who’s next?
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The Adderall Diaries
Writer and Adderall enthusiast Stephen Elliott…
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The truth is a motherf_cker.
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Cape Fear
Sam Bowden is a small-town corporate attorney….
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11/15/91
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There is nothing in the dark that isn’t there …
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The New Adventures of Pippi Longstocking
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Come see Pippi as you’ve never seen her before!
The New Adventures of Pippi Longstocking
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The Accountant
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10/14/16
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Calculate your choices.
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הסיפור של יוסי
The sequel to “Yossi and Jagger” finds charact…
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Yossi
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American Outlaws
When a Midwest town learns that a corrupt rail…
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8/17/01
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Sometimes the wrong side of the law is the rig…
American Outlaws
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Jane Got a Gun
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Jane Got a Gun
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Doomsday
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Mankind has an expiration date.
Doomsday
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The Omega Code
In this spiritual thriller, an ancient prophec…
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The Omega Code
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Burlesque
The Burlesque Lounge has its best days behind …
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11/23/10
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It takes a legend… to make a star
Burlesque
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Hugo
Hugo is an orphan boy living in the walls of a…
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11/22/11
126.0
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One of the most legendary directors of our tim…
Hugo
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Carrie
Carrie may be ostracized, but the shy teen has…
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11/3/76
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If you’ve got a taste for terror… take Carri…
Carrie
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Thelma & Louise
Whilst on a short weekend getaway, Louise shoo…
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5/24/91
130.0
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Somebody said get a life… so they did
Thelma & Louise
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L’imbalsamatore
Peppino is an aging taxidermist constantly rid…
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Kelly’s Heroes
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6/22/70
144.0
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They set out to rob a bank… and damn near wo…
Kelly’s Heroes
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No Good Deed
Terri is a devoted wife and mother of two, liv…
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9/10/14
83.0
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First he gets into your house. Then he gets in…
No Good Deed
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Lord of War
Yuri Orlov is a globetrotting arms dealer and,…
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9/16/05
122.0
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Where there’s a will, there’s a weapon
Lord of War
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Barely Lethal
A 16-year-old international assassin yearning …
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4/30/15
96.0
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Click. Clique. Bang.
Barely Lethal
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Popstar: Never Stop Never Stopping
When his new album fails to sell records, pop/…
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6/3/16
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Popstar: Never Stop Never Stopping
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Woodstock
An intimate look at the Woodstock Music & Art …
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3/26/70
225.0
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3 days of peace, music…and love.
Woodstock
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Dikari
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11/23/06
100.0
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Savages
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Disconnect
Disconnect interweaves multiple storylines abo…
12.188120
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9/11/12
115.0
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Look up
Disconnect
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No Way Out
Navy Lt. Tom Farrell meets a young woman, Susa…
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8/14/87
114.0
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Is it a crime of passion, or an act of treason?
No Way Out
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http://www.howhighmovie.com/
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How High
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12/21/01
93.0
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They’ve got their degrees all rolled up.
How High
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Scanners II: The New Order
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[{‘iso_3166_1’: ‘CA’, ‘name’: ‘Canada’}]
6/28/91
104.0
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He can make you do anything…if he puts his m…
Scanners II: The New Order
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The Art of the Steal
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8.436614
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[{‘iso_3166_1’: ‘CA’, ‘name’: ‘Canada’}]
9/11/13
90.0
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It takes a great artist to pull off the perfec…
The Art of the Steal
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Special
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1/30/06
81.0
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Special
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http://www.inappropriatecomedy.com
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InAPPropriate Comedy
A no-nonsense cop has a flair for fashion and …
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3/22/13
84.0
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An equal opportunity offender.
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Whore
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6/21/91
85.0
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If You’re Afraid to Say It… Just See It.
Whore
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http://www.weareallprecious.com/
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Precious
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11/6/09
110.0
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Life is hard. Life is short. Life is painful. …
Precious
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http://www.terminatormovie.com/
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Terminator Genisys
The year is 2029. John Connor, leader of the r…
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6/23/15
126.0
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Reset the future
Terminator Genisys
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Original Sin
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8/3/01
118.0
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This is not a love story – it’s a story about …
Original Sin
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Without a Paddle
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8/20/04
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The call of the wild, the thrill of adventure….
Without a Paddle
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The Verdict
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12/8/82
129.0
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The doctors want to settle, the Church wants t…
The Verdict
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It Follows
For 19-year-old Jay, fall should be about scho…
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2/4/15
100.0
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It doesn’t think, it doesn’t feel, it doesn’t …
It Follows
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Vivre sa vie: film en douze tableaux
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9/20/62
85.0
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Vivre Sa Vie
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