From cab536f7f25ca6c9bd062a90c9127fa72f9d2c0c Mon Sep 17 00:00:00 2001 From: reedts Date: Thu, 21 Jun 2018 14:05:30 +0200 Subject: [PATCH] Fixed a few things --- pywatts/main.py | 39 +++++++++++++++++++++++++++------------ pywatts/neural.py | 6 +++--- 2 files changed, 30 insertions(+), 15 deletions(-) diff --git a/pywatts/main.py b/pywatts/main.py index 04815ed..b509c22 100644 --- a/pywatts/main.py +++ b/pywatts/main.py @@ -4,15 +4,16 @@ import matplotlib.pyplot as pp import pywatts.neural from sklearn.metrics import explained_variance_score, mean_absolute_error, median_absolute_error import pandas +from random import randint from sklearn.model_selection import train_test_split df = pywatts.db.rows_to_df(list(range(1, 50))) -X = df[[col for col in df.columns if col != 'dc']] +X = df y = df['dc'] -X_train, X_tmp, y_train, y_tmp = train_test_split(X, y, test_size=0.2, random_state=23) +X_train, X_tmp, y_train, y_tmp = train_test_split(X, y, test_size=0.2, random_state=34) X_test, X_val, y_test, y_val = train_test_split(X_tmp, y_tmp, test_size=0.5, random_state=23) @@ -20,20 +21,34 @@ feature_cols = [tf.feature_column.numeric_column(col) for col in X.columns] n = pywatts.neural.Net(feature_cols=feature_cols) -def input_data(json_str): +def train_split(data, size): + X_values = {'dc': [], 'temp': [], 'wind': []} + y_values = [] + for i in range(size): + rnd_idx = randint(0, data.size / data.shape[1] - 337) + + X_values['dc'].extend(data['dc'][rnd_idx:rnd_idx + 336]) + X_values['temp'].extend(data['temp'][rnd_idx:rnd_idx + 336]) + X_values['wind'].extend(data['wind'][rnd_idx:rnd_idx + 336]) + y_values.append(data['dc'][rnd_idx + 337]) + + return pandas.DataFrame.from_dict(X_values), pandas.DataFrame.from_dict({'dc': y_values}) + + +def input_data(json_str, idx=0): tmp_df = pandas.read_json(json_str) return pandas.DataFrame.from_dict( - {'dc': [x for l in tmp_df['dc'] for x in l], - 'temp': [x for l in tmp_df['temp'] for x in l], - 'wind': [x for l in tmp_df['wind'] for x in l]} + {'dc': tmp_df['dc'][idx], + 'temp': tmp_df['temp'][idx], + 'wind': tmp_df['wind'][idx]} ) def train(steps=100): evaluation = [] for i in range(steps): - n.train(X_train, y_train, steps=400) + n.train(X_train, y_train, steps=100) evaluation.append(n.evaluate(X_val, y_val)) print("Training %s of %s" % ((i+1), steps)) return evaluation @@ -42,7 +57,7 @@ def train(steps=100): def plot_training(evaluation): loss = [] for e in evaluation: - loss.append(e['loss']) + loss.append(e['average_loss']) pp.plot(loss) @@ -52,11 +67,11 @@ def predict(X_pred): return predictions -def eval_prediction(prediction): +def eval_prediction(prediction, result): print("The Explained Variance: %.2f" % explained_variance_score( - y_test, prediction)) + result, prediction)) print("The Mean Absolute Error: %.2f volt dc" % mean_absolute_error( - y_test, prediction)) + result, prediction)) print("The Median Absolute Error: %.2f volt dc" % median_absolute_error( - y_test, prediction)) + result, prediction)) diff --git a/pywatts/neural.py b/pywatts/neural.py index 673adc9..c1fbfc5 100644 --- a/pywatts/neural.py +++ b/pywatts/neural.py @@ -1,7 +1,7 @@ import tensorflow as tf -def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=400): +def pywatts_input_fn(X, y=None, num_epochs=None, shuffle=True, batch_size=366): return tf.estimator.inputs.pandas_input_fn(x=X, y=y, num_epochs=num_epochs, @@ -15,11 +15,11 @@ class Net: def __init__(self, feature_cols=__feature_cols): self.__regressor = tf.estimator.DNNRegressor(feature_columns=feature_cols, - hidden_units=[50, 50], + hidden_units=[2], model_dir='tf_pywatts_model') def train(self, training_data, training_results, steps): - self.__regressor.train(input_fn=pywatts_input_fn(training_data, y=training_results, num_epochs=None, shuffle=True), steps=steps) + self.__regressor.train(input_fn=pywatts_input_fn(training_data, y=training_results, num_epochs=None, shuffle=True, batch_size=336), steps=steps) def evaluate(self, eval_data, eval_results): return self.__regressor.evaluate(input_fn=pywatts_input_fn(eval_data, y=eval_results, num_epochs=1, shuffle=False), steps=1)