Python sklearn.metrics.classification.accuracy_score() Examples

The following are 8 code examples of sklearn.metrics.classification.accuracy_score(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.metrics.classification , or try the search function .
Example #1
Source File: Stock_Prediction_Model_DBN.py    From StockRecommendSystem with MIT License 5 votes vote down vote up 
def predict(self, model, X, y):
        Y_pred = model.predict_proba_dict(X) 
        df = pd.DataFrame(Y_pred).values
        print('Accuracy: ', accuracy_score(y, np.argmax(df, axis=1)))
        return df
Example #2
Source File: Stock_Prediction_Recommand_System.py    From StockRecommendSystem with MIT License 5 votes vote down vote up 
def predict(self, model, X, y, ContinuousColumnName, CategoricalColumnName):
        predictions = np.array(list(model.predict_proba(input_fn=lambda: self.input_fn(X, y, ContinuousColumnName, CategoricalColumnName))))
        
        results = model.evaluate(input_fn=lambda: self.input_fn(X, y, ContinuousColumnName, CategoricalColumnName), steps=1)

        for key in sorted(results):
            print("%s: %s"%(key, results[key]))
        print('Accuracy: ', accuracy_score(y, tf.argmax(predictions, axis=1)))
        return predictions
Example #3
Source File: Stock_Prediction_Model_Random_Forrest.py    From StockRecommendSystem with MIT License 5 votes vote down vote up 
def predict(self, model, X, y):
        predictions = model.predict_proba(X)
        if np.isfinite(y).all():
            print('Accuracy: ', accuracy_score(y, np.argmax(predictions, axis=1)))
        return predictions
Example #4
Source File: prediction.py    From multi-categorical-gans with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def prediction_score(train_X, train_y, test_X, test_y, metric, model):
    # if the train labels are always the same
    values_train = set(train_y)
    if len(values_train) == 1:
        # predict always that value
        only_value_train = list(values_train)[0]
        test_pred = np.ones_like(test_y) * only_value_train

    # if the train labels have different values
    else:
        # create the model
        if model == "random_forest_classifier":
            m = RandomForestClassifier(n_estimators=10)
        elif model == "logistic_regression":
            m = LogisticRegression()
        else:
            raise Exception("Invalid model name.")

        # fit and predict
        m.fit(train_X, train_y)
        test_pred = m.predict(test_X)

    # calculate the score
    if metric == "f1":
        return f1_score(test_y, test_pred)
    elif metric == "accuracy":
        return accuracy_score(test_y, test_pred)
    else:
        raise Exception("Invalid metric name.")
Example #5
Source File: model.py    From polyaxon-examples with Apache License 2.0 4 votes vote down vote up 
def train_and_eval(ngram_range=(1, 1), max_features=None, max_df=1.0, C=1.0):
    """Train and eval newsgroup classification.

    :param ngram_range: ngram range
    :param max_features: the number of maximum features
    :param max_df: max document frequency ratio
    :param C: Inverse of regularization strength for LogisticRegression
    :return: metrics
    """
    # Loads train and test data.
    train_data = fetch_20newsgroups(subset='train')
    test_data = fetch_20newsgroups(subset='test')

    # Define the pipeline.
    pipeline = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', LogisticRegression(multi_class='auto'))
    ])

    # Set pipeline parameters.
    params = {
        'tfidf__ngram_range': ngram_range,
        'tfidf__max_features': max_features,
        'tfidf__max_df': max_df,
        'clf__C': C,
    }
    pipeline.set_params(**params)
    print(pipeline.get_params().keys())

    # Train the model.
    pipeline.fit(train_data.data, train_data.target)
    # Predict test data.
    start_time = time()
    predictions = pipeline.predict(test_data.data)
    inference_time = time() - start_time
    avg_inference_time = 1.0 * inference_time / len(test_data.target)
    print("Avg. inference time: {}".format(avg_inference_time))

    # Calculate the metrics.
    accuracy = accuracy_score(test_data.target, predictions)
    recall = recall_score(test_data.target, predictions, average='weighted')
    f1 = f1_score(test_data.target, predictions, average='weighted')
    metrics = {
        'accuracy': accuracy,
        'recall': recall,
        'f1': f1,
    }

    return metrics
Example #6
Source File: model.py    From polyaxon-examples with Apache License 2.0 4 votes vote down vote up 
def train_and_eval(output, ngram_range=(1, 1), max_features=None, max_df=1.0, C=1.0):
    """Train and eval newsgroup classification.

    :param ngram_range: ngram range
    :param max_features: the number of maximum features
    :param max_df: max document frequency ratio
    :param C: Inverse of regularization strength for LogisticRegression
    :return: metrics
    """
    # Loads train and test data.
    train_data = fetch_20newsgroups(subset='train')
    test_data = fetch_20newsgroups(subset='test')

    # Define the pipeline.
    pipeline = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', LogisticRegression(multi_class='auto'))
    ])

    # Set pipeline parameters.
    params = {
        'tfidf__ngram_range': ngram_range,
        'tfidf__max_features': max_features,
        'tfidf__max_df': max_df,
        'clf__C': C,
    }
    pipeline.set_params(**params)
    print(pipeline.get_params().keys())

    # Train the model.
    pipeline.fit(train_data.data, train_data.target)
    # Predict test data.
    start_time = time()
    predictions = pipeline.predict(test_data.data)
    inference_time = time() - start_time
    avg_inference_time = 1.0 * inference_time / len(test_data.target)
    print("Avg. inference time: {}".format(avg_inference_time))

    # Calculate the metrics.
    accuracy = accuracy_score(test_data.target, predictions)
    recall = recall_score(test_data.target, predictions, average='weighted')
    f1 = f1_score(test_data.target, predictions, average='weighted')
    metrics = {
        'accuracy': accuracy,
        'recall': recall,
        'f1': f1,
    }

    # Persistent the model.
    joblib.dump(pipeline, output)

    return metrics
Example #7
Source File: model.py    From polyaxon with Apache License 2.0 4 votes vote down vote up 
def train_and_eval(ngram_range=(1, 1), max_features=None, max_df=1.0, C=1.0):
    """Train and eval newsgroup classification.

    :param ngram_range: ngram range
    :param max_features: the number of maximum features
    :param max_df: max document frequency ratio
    :param C: Inverse of regularization strength for LogisticRegression
    :return: metrics
    """
    # Loads train and test data.
    train_data = fetch_20newsgroups(subset='train')
    test_data = fetch_20newsgroups(subset='test')

    # Define the pipeline.
    pipeline = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', LogisticRegression(multi_class='auto'))
    ])

    # Set pipeline parameters.
    params = {
        'tfidf__ngram_range': ngram_range,
        'tfidf__max_features': max_features,
        'tfidf__max_df': max_df,
        'clf__C': C,
    }
    pipeline.set_params(**params)
    print(pipeline.get_params().keys())

    # Train the model.
    pipeline.fit(train_data.data, train_data.target)
    # Predict test data.
    start_time = time()
    predictions = pipeline.predict(test_data.data)
    inference_time = time() - start_time
    avg_inference_time = 1.0 * inference_time / len(test_data.target)
    print("Avg. inference time: {}".format(avg_inference_time))

    # Calculate the metrics.
    accuracy = accuracy_score(test_data.target, predictions)
    recall = recall_score(test_data.target, predictions, average='weighted')
    f1 = f1_score(test_data.target, predictions, average='weighted')
    metrics = {
        'accuracy': accuracy,
        'recall': recall,
        'f1': f1,
    }

    return metrics
Example #8
Source File: model.py    From polyaxon with Apache License 2.0 4 votes vote down vote up 
def train_and_eval(output, ngram_range=(1, 1), max_features=None, max_df=1.0, C=1.0):
    """Train and eval newsgroup classification.

    :param ngram_range: ngram range
    :param max_features: the number of maximum features
    :param max_df: max document frequency ratio
    :param C: Inverse of regularization strength for LogisticRegression
    :return: metrics
    """
    # Loads train and test data.
    train_data = fetch_20newsgroups(subset='train')
    test_data = fetch_20newsgroups(subset='test')

    # Define the pipeline.
    pipeline = Pipeline([
        ('tfidf', TfidfVectorizer()),
        ('clf', LogisticRegression(multi_class='auto'))
    ])

    # Set pipeline parameters.
    params = {
        'tfidf__ngram_range': ngram_range,
        'tfidf__max_features': max_features,
        'tfidf__max_df': max_df,
        'clf__C': C,
    }
    pipeline.set_params(**params)
    print(pipeline.get_params().keys())

    # Train the model.
    pipeline.fit(train_data.data, train_data.target)
    # Predict test data.
    start_time = time()
    predictions = pipeline.predict(test_data.data)
    inference_time = time() - start_time
    avg_inference_time = 1.0 * inference_time / len(test_data.target)
    print("Avg. inference time: {}".format(avg_inference_time))

    # Calculate the metrics.
    accuracy = accuracy_score(test_data.target, predictions)
    recall = recall_score(test_data.target, predictions, average='weighted')
    f1 = f1_score(test_data.target, predictions, average='weighted')
    metrics = {
        'accuracy': accuracy,
        'recall': recall,
        'f1': f1,
    }

    # Persistent the model.
    joblib.dump(pipeline, output)

    return metrics