Created
November 7, 2017 15:38
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build, evaluate, and save scikitlearn pipeline
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| import os | |
| import time | |
| import string | |
| import pickle | |
| import pandas as pd | |
| from operator import itemgetter | |
| from nltk.corpus import stopwords as sw | |
| from nltk.corpus import wordnet as wn | |
| from nltk import wordpunct_tokenize | |
| from nltk import WordNetLemmatizer | |
| from nltk import sent_tokenize | |
| from nltk import pos_tag | |
| from sklearn.pipeline import Pipeline | |
| from sklearn.preprocessing import LabelEncoder | |
| from sklearn.linear_model import SGDClassifier | |
| from sklearn.base import BaseEstimator, TransformerMixin | |
| from sklearn.metrics import classification_report as clsr | |
| from sklearn.feature_extraction.text import TfidfVectorizer | |
| from sklearn.cross_validation import train_test_split as tts | |
| from sklearn.linear_model import Perceptron | |
| from nltk_preprocessor import NLTKPreprocessor | |
| from sklearn import metrics | |
| def timeit(func): | |
| def wrapper(*args, **kwargs): | |
| start = time.time() | |
| result = func(*args, **kwargs) | |
| delta = time.time() - start | |
| return result, delta | |
| return wrapper | |
| def identity(arg): | |
| return arg | |
| # implement gridsearch | |
| # http://scikit-learn.org/stable/auto_examples/plot_compare_reduction.html#sphx-glr-auto-examples-plot-compare-reduction-py | |
| @timeit | |
| def build_and_evaluate(X, y, classifier=SGDClassifier, outpath=None, verbose=True, cachedir=None): | |
| @timeit | |
| def build(classifier, X, y=None): | |
| model = Pipeline([ | |
| ('preprocessor', NLTKPreprocessor()), | |
| ('vectorizer', TfidfVectorizer( | |
| tokenizer=identity, preprocessor=None, lowercase=False)), | |
| ('classifier', classifier), | |
| ], memory=cachedir) | |
| model.fit(X, y) | |
| return model | |
| # # Label encode the targets | |
| labels = LabelEncoder() | |
| y = labels.fit_transform(y) | |
| target_names = list(map(str, labels.classes_)) | |
| # Begin evaluation | |
| if verbose: | |
| print("Building for evaluation") | |
| X_train, X_test, y_train, y_test = tts(X, y, test_size=0.2, random_state=0, stratify=y) | |
| model, secs = build(classifier, X_train, y_train) | |
| if verbose: | |
| print("Evaluation model fit in {:0.3f} seconds".format(secs)) | |
| if verbose: | |
| print("Classification Report:\n") | |
| y_pred = model.predict(X_test) | |
| print(clsr(y_test, y_pred, target_names=target_names)) | |
| print(metrics.confusion_matrix(y_test, y_pred)) | |
| if verbose: | |
| print("Building complete model and saving ...") | |
| model, secs = build(classifier, X, y) | |
| model.labels_ = labels | |
| if verbose: | |
| print("Complete model fit in {:0.3f} seconds".format(secs)) | |
| if outpath: | |
| with open(outpath, 'wb') as f: | |
| pickle.dump(model, f) | |
| print("Model written out to {}".format(outpath)) | |
| return model |
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