"""Functions to preprocess vectorized data, split into training and validation
sets, and train a model.
"""

from . import filters


def preprocess(sv, gv, min_genre_count=1):
    """Given summary and genre vectorizations, return filtered matrices
    containing 1) only genres with at least the minimum count
    (well-populated genres), and 2) only movies with at least one
    well-populated genre.
    """
    gf = filters.genres(filters.genre_counts(gv), min_genre_count)
    mf = filters.movies(gv, gf)
    return (sv[mf, :], gv[:, gf])

@@ -12,36 +12,8 @@
 s to
- preprocess vectorized data,
  spl
@@ -43,17 +43,17 @@
 lidation
-%0A
+ 
 sets, an
@@ -78,22 +78,65 @@
 %22%22%0A%0A
-from .
+import scipy%0Afrom sklearn.feature_extraction.text
  import 
 filt
@@ -135,394 +135,2232 @@
 ort 
+T
 fi
-lters%0A%0A%0Adef preprocess(sv, gv, min_genre_count=1):%0A    %22%22%22Given summary and genre vectorizations, return filtered matrices%0A    containing 1) only genres with at least the minimum count%0A    (well-populated genres), and 2) only movies with at least one%0A    well-populated genre.%0A    %22%22%22%0A    gf = filters.genres(filters.genre_counts(gv), min_genre_count)%0A    mf = filters.movies(gv, gf
+dfVectorizer%0Afrom sklearn.pipeline import Pipeline%0Afrom sklearn.multiclass import OneVsRestClassifier%0Afrom sklearn.model_selection import train_test_split%0A%0ASUMMARY_TOKEN_PATTERN = r%22%5Cb%5Ba-zA-Z%5D+%5Cb%22%0AMAX_DOC_FREQ = 0.25%0A%0A%0Adef pipeline(%0A    binary_classifier,%0A    max_doc_freq=MAX_DOC_FREQ,%0A    summary_token_pattern=SUMMARY_TOKEN_PATTERN,%0A):%0A    %22%22%22Given an sklearn binary classifier and other application-specific%0A    parameters, return an sklearn transformation and classification Pipeline%0A    object with fit and predict methods.%0A%0A    Notes%0A    -----%0A    * This pipeline is specifically for multi-label classification using the%0A      One-vs-All model.%0A    %22%22%22%0A    tfidf = TfidfVectorizer(%0A        max_df=max_doc_freq,%0A        token_pattern=summary_token_pattern,%0A        stop_words='english')%0A    clf = OneVsRestClassifier(binary_classifier, n_jobs=-1)%0A    return Pipeline(%5B('tfidf', tfidf), ('clf', clf)%5D)%0A%0A%0Adef split_data(x, y, test_size=0.25, stratify_split=False):%0A    %22%22%22Given an array of movie summaries and a sparse matrix of genre indicator%0A    vectors, return a random training/test split of the data.%0A%0A    Parameters%0A    ----------%0A    x: array of inputs%0A    y: array (or sparse matrix) of outputs%0A    test_size: fraction of data used for validation%0A    stratify: if True, apply stratification to ensure train and test sets have%0A        similar class distributions%0A%0A    Returns%0A    -------%0A    %7B%0A        'x_train': %3Cinput training data%3E,%0A        'x_test': %3Cinput validation data%3E,%0A        'y_train': %3Coutput training data%3E,%0A        'y_test': %3Couput validation data%3E,%0A%0A    %7D%0A%0A    Notes%0A    -----%0A    * Stratification doesn't seem to work when one of the inputs is a sparse%0A      matrix (seems like a bug).  The genre_vectors matrix must be converted%0A      to a dense array before stratification is applied.%0A    %22%22%22%0A%0A    xx = x.copy()%0A    yy = y.copy()%0A    stratify = None%0A    if stratify_split is not None:%0A        if isinstance(xx, scipy.sparse.csr.csr_matrix):%0A            xx = xx.toarray()%0A        if isinstance(yy, scipy.sparse.csr.csr_matrix):%0A            yy = yy.toarray()%0A        stratify = yy%0A%0A    xtrain, xtest, ytrain, ytest = train_test_split(%0A        xx, yy, test_size=test_size, stratify=stratify
 )%0A
+%0A
     
@@ -2370,27 +2370,110 @@
 urn 
-(sv%5Bmf, :%5D, gv%5B:, gf%5D)
+%7B%0A        'x_train': xtrain,%0A        'x_test': xtest,%0A        'y_train': ytrain,%0A        'y_test': ytest%7D
 %0A

add pipeline instantiation and data splitting functions