From bf3d63299de5d1ebe2977796a211ea418335c115 Mon Sep 17 00:00:00 2001 From: Nabeel Date: Mon, 9 Mar 2020 20:25:08 +1100 Subject: [PATCH 1/5] Updates for stationarity Improvements and fixes for making the target and lags stationary --- core/_machine_learning.py | 78 ++++++++++++++++------- core/_sklearn.py | 126 +++++++++++++++++++++++++------------- core/_utils.py | 39 ++++++++++++ 3 files changed, 179 insertions(+), 64 deletions(-) diff --git a/core/_machine_learning.py b/core/_machine_learning.py index 98e1ce0..28b961f 100644 --- a/core/_machine_learning.py +++ b/core/_machine_learning.py @@ -745,7 +745,7 @@ class TargetTransformer: An inverse transform method allows for reversing the transformations. """ - def __init__(self, scale=True, make_stationary=None, missing="zeros", scaler="StandardScaler", logfile=None, **kwargs): + def __init__(self, scale=True, make_stationary=None, stationarity_lags=[1], missing="zeros", scaler="StandardScaler", logfile=None, **kwargs): """ Initialize the TargetTransformer instance. @@ -754,8 +754,8 @@ def __init__(self, scale=True, make_stationary=None, missing="zeros", scaler="St make_stationary is a parameter to determine if the target will be made stationary. This should only be used for sequential data. Passing make_stationary='log' will apply a logarithm to the target and use an exponential for the inverse transform. Passing make_stationary='difference' will difference the values to make the target series stationary. - By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list of lags as a kwarg. - e.g. lags=[1, 12] + By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list using the stationarity_lags parameter. + e.g. stationarity_lags=[1, 12] missing deteremines how missing values are dealt with before the scaling is applied. Valid options specified through the missing parameter are: zeros, mean, median, mode @@ -770,19 +770,38 @@ def __init__(self, scale=True, make_stationary=None, missing="zeros", scaler="St self.missing = missing self.scaler = scaler self.logfile = logfile - - if make_stationary and 'lags' in kwargs: - self.lags = kwargs.pop('lags') - else: - self.lags = [1] - + self.lags = stationarity_lags self.kwargs = kwargs def fit(self, y): """ + Make the data stationary if required. Fit the scaler to target values from the training set. """ + # Apply a logarithm to make the array stationary + if self.make_stationary == 'log': + y = np.log(y) + + # Apply stationarity lags by differencing the array + elif self.make_stationary == 'difference': + y_diff = y.copy() + len_y = len(y_diff) + + for i in range(max(self.lags), len_y): + for lag in self.lags: + if isinstance(y_diff, (pd.Series, pd.DataFrame)): + y_diff.iloc[i] = y_diff.iloc[i] - y.iloc[i - lag] + else: + y_diff[i] = y_diff[i] - y[i - lag] + + # Remove targets with insufficient lag periods + if isinstance(y_diff, (pd.Series, pd.DataFrame)): + y = y_diff.iloc[max(self.lags):] + else: + y = y_diff[max(self.lags):] + + # Fit the scaler if self.scale: # Get an instance of the sklearn scaler fit to y self.scaler_instance = utils.get_scaler(y, missing=self.missing, scaler=self.scaler, **self.kwargs) @@ -798,15 +817,7 @@ def transform(self, y, array_like=True): Note that the differencing won't be done if array_like=False. """ - y_transform = y - - # Scale the targets using the previously fit scaler - if self.scale: - y_transform = self.scaler_instance.transform(y) - - if isinstance(y, pd.DataFrame): - # The scaler returns a numpy array which needs to be converted back to a data frame - y_transform = pd.DataFrame(y_transform, columns=y.columns, index=y.index) + y_transform = y # Apply a logarithm to make the array stationary if self.make_stationary == 'log': @@ -831,6 +842,14 @@ def transform(self, y, array_like=True): else: y_transform = y_diff[max(self.lags):] + # Scale the targets using the previously fit scaler + if self.scale: + y_transform = self.scaler_instance.transform(y) + + if isinstance(y, pd.DataFrame): + # The scaler returns a numpy array which needs to be converted back to a data frame + y_transform = pd.DataFrame(y_transform, columns=y.columns, index=y.index) + if self.logfile is not None: self._print_log(1, data=y_transform, array_like=array_like) @@ -849,8 +868,21 @@ def inverse_transform(self, y_transform, array_like=True): array_like determines if y_transform is expected to be multiple values or a single value. Note that the differencing won't be done if array_like=False. + + For an inverse of differencing, y_transform needs to include sufficient actual lag values. """ + # If inversing differencing, we assume sufficient lag values. + # These should be actual values that have not been scaled previously. + if self.make_stationary == 'difference' and array_like: + if isinstance(y_transform, pd.DataFrame): + y_lags = y_transform.iloc[:max(self.lags)] + y = y_transform.iloc[max(self.lags):] + else: + y_lags = y_transform[:max(self.lags)] + y = y_transform[max(self.lags):] + + # Inverse the scaling for non-lag values if self.scale: if isinstance(y_transform, pd.DataFrame): y = self.scaler_instance.inverse_transform(np.reshape(y_transform.values, (-1, 1))) @@ -864,10 +896,14 @@ def inverse_transform(self, y_transform, array_like=True): y = np.exp(y_transform) # Reverse the differencing applied during transform - # NOTE: y_transform will need to include actual values preceding the lags + # NOTE: y_transform will need to include actual lag values elif self.make_stationary == 'difference' and array_like: - y = y_transform.copy() - len_y = len(y_transform) + if isinstance(y, (pd.Series, pd.DataFrame)): + y = pd.concat([y_lags, y]) + else: + y = np.append(y_lags, y) + + len_y = len(y) for i in range(max(self.lags), len_y): for lag in self.lags: diff --git a/core/_sklearn.py b/core/_sklearn.py index b8ec28b..36a5df2 100644 --- a/core/_sklearn.py +++ b/core/_sklearn.py @@ -523,16 +523,6 @@ def fit(self): err = "Incorrect usage. The estimator specified is not a known classifier or regressor: {0}".format(self.model.estimator) raise Exception(err) - # Add lag observations to the samples if required - if self.model.lags or self.model.lag_target: - # Check if the current sample will be included as an input, or whether we only use lag observations for predictions - extrapolate = 1 if self.model.current_sample_as_input else 0 - # Add the lag observations - train_test_df = self._add_lags(train_test_df, target_df, extrapolate=extrapolate, update_features_df=True) - # Drop targets for samples which were dropped due to null values after adding lags. - if len(target_df) > len(train_test_df): - target_df = target_df.iloc[len(target_df)-len(train_test_df):] - # Check which validation strategy is to be used, if any # For an explanation of cross validation in scikit-learn see: http://scikit-learn.org/stable/modules/cross_validation.html#multimetric-cross-validation if self.model.time_series_split > 0: @@ -568,8 +558,8 @@ def fit(self): # Scale the targets and increase stationarity if required if self.model.scale_target or self.model.make_stationary: # Set up the target transformer - self.model.target_transformer = TargetTransformer(scale=self.model.scale_target, make_stationary=self.model.make_stationary, missing=self.model.missing,\ - scaler=self.model.scaler, logfile=self.logfile, **self.model.scaler_kwargs) + self.model.target_transformer = TargetTransformer(scale=self.model.scale_target, make_stationary=self.model.make_stationary, stationarity_lags=self.model.stationarity_lags,\ + missing=self.model.missing, scaler=self.model.scaler, logfile=self.logfile, **self.model.scaler_kwargs) # Fit the transformer to the training targets self.model.target_transformer = self.model.target_transformer.fit(self.y_train) @@ -578,13 +568,17 @@ def fit(self): self.y_train = self.model.target_transformer.transform(self.y_train) # Drop samples where the target cannot be transformed due to insufficient lags self.X_train = self.X_train.iloc[len(self.X_train)-len(self.y_train):] - # Do the same for the test set if applicable - try: - self.y_test = self.model.target_transformer.transform(self.y_test) - self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):] - except AttributeError: - pass + # Add lag observations to the samples if required + if self.model.lags or self.model.lag_target: + # Check if the current sample will be included as an input, or whether we only use lag observations for predictions + extrapolate = 1 if self.model.current_sample_as_input else 0 + # Add the lag observations + self.X_train = self._add_lags(self.X_train, self.y_train, extrapolate=extrapolate, update_features_df=True) + # Drop targets for samples which were dropped due to null values after adding lags. + if len(self.y_train) > len(self.X_train): + self.y_train = self.y_train.iloc[len(self.y_train)-len(self.X_train):] + # If this is a Keras estimator, we require the preprocessing to return a data frame instead of a numpy array prep_return = 'df' if self.model.using_keras else 'np' @@ -901,12 +895,19 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Open an existing model and get the training & test dataset and targets based on the request self.X_test, self.y_test = self._get_model_and_data(target=True, ordered_data=ordered_data) - # Scale the targets and increase stationarity if required - if self.model.scale_target or self.model.make_stationary: - # Apply the transformer to the test targets - self.y_test = self.model.target_transformer.transform(self.y_test) - # Drop samples where self.y_test cannot be transformed due to insufficient lags - self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):] + # Keep a copy of the y_test before any transformations + y_test_copy = self.y_test.copy() + # Calculate if any extra lag periods are expected when making the target stationary using differencing + extra_lags = max(self.model.stationarity_lags) if self.model.lag_target and self.model.make_stationary=='difference' else 0 + + # Scale the targets and increase stationarity if required + if self.model.scale_target or self.model.make_stationary: + # If using differencing, we assume sufficient lag values for inversing the transformation later + y_lags = self.y_test.iloc[:extra_lags].values if self.model.make_stationary=='difference' else None + # Apply the transformer to the test targets + self.y_test = self.model.target_transformer.transform(self.y_test) + # Drop samples where self.y_test cannot be transformed due to insufficient lags + self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):] # Refresh the keras model to avoid tensorflow errors if self.model.using_keras: @@ -915,10 +916,19 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Get predictions based on the samples if ordered_data: self.y_pred = self.sequence_predict(variant="internal") - + # Handle possible null values where a prediction could not be generated self.y_pred = self.y_pred[self.placeholders:] - self.y_test = self.y_test.iloc[self.placeholders:] + self.y_test = y_test_copy.iloc[self.placeholders+extra_lags:] + + # Inverse transformations predictions if required + if self.model.scale_target or self.model.make_stationary: + # Apply the transformer to the test targets + self.y_pred = self.y_pred if y_lags is None else np.append(y_lags, self.y_pred) + self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) + # Remove lags used for making the series stationary in case of differencing + if self.model.make_stationary == 'difference': + self.y_pred = self.y_pred[extra_lags:] else: self.y_pred = self.model.pipe.predict(self.X_test) @@ -926,11 +936,8 @@ def calculate_metrics(self, caller="external", ordered_data=False): if self.model.scale_target or self.model.make_stationary: # Apply the transformer to the predictions self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) - - # Inverse transformations on the test targets if required - if self.model.scale_target or self.model.make_stationary: - # Apply the transformer to the test targets - self.y_test = self.model.target_transformer.inverse_transform(self.y_test) + # Reset y_test to orginal values + self.y_test = y_test_copy # Flatten the y_test DataFrame self.y_test = self.y_test.values.ravel() @@ -1189,6 +1196,8 @@ def sequence_predict(self, load_script=False, variant="predict"): # Scale the targets and increase stationarity if required if variant != 'internal' and self.model.lag_target and (self.model.scale_target or self.model.make_stationary): + # If using differencing, we assume sufficient lag values for inversing the transformation later + y_lags = y.iloc[:max(self.model.stationarity_lags)].values if self.model.make_stationary=='difference' else None # Apply the transformer to the targets y = self.model.target_transformer.transform(y) # Drop samples where y cannot be transformed due to insufficient lags @@ -1198,6 +1207,7 @@ def sequence_predict(self, load_script=False, variant="predict"): prediction_periods = self.model.prediction_periods # Set the number of rows required for one prediction rows_per_pred = 1 + extra_lags = max(self.model.stationarity_lags) if self.model.lag_target and self.model.make_stationary=='difference' else 0 # Check that the input data includes history to meet any lag calculation requirements if self.model.lags: @@ -1205,9 +1215,12 @@ def sequence_predict(self, load_script=False, variant="predict"): extrapolate = 1 if self.model.current_sample_as_input else 0 # An additional lag observation is needed if previous targets are being added to the features rows_per_pred = self.model.lags+extrapolate+1 if self.model.lag_target else self.model.lags+extrapolate + # If the target is being lagged and made stationary through differencing additional lag periods are required + if self.model.lag_target and self.model.make_stationary=='difference': + extra_msg = " plus an additional {} periods for making the target stationary using differencing".format(extra_lags) # For multi-step predictions we only expect lag values, not the current period's values # rows_per_pred = rows_per_pred-1 if prediction_periods > 1 else rows_per_pred - assert len(X) >= rows_per_pred, "Insufficient input data as the model requires {} lag periods for each prediction".format(rows_per_pred) + assert len(X) >= rows_per_pred + extra_lags, "Insufficient input data as the model requires {} lag periods for each prediction".format(rows_per_pred) + extra_msg if variant != 'internal': # Prepare the response DataFrame @@ -1296,10 +1309,10 @@ def sequence_predict(self, load_script=False, variant="predict"): if get_proba: # Get the predicted probability for each sample probabilities = self.model.pipe.predict_proba(X) - + # Set the number of placeholders needed in the response # These are samples for which predictions were not generated due to insufficient lag periods or for meeting multi-step prediction period requirements - self.placeholders = rows_per_pred + self.placeholders = rows_per_pred + extra_lags # Transform probabilities to a readable string if get_proba: # Add the required number of placeholders at the start of the response list @@ -1316,14 +1329,12 @@ def sequence_predict(self, load_script=False, variant="predict"): s = s + ", {0}: {1:.3f}".format(self.model.pipe.named_steps['estimator'].classes_[i], b) i += 1 y.append(s[2:]) + # Prepare predictions else: if prediction_periods > 1: # Set the value to use for nulls - if is_numeric_dtype(np.array(predictions)): - null = np.NaN - else: - null = "\x00" + null = np.NaN if is_numeric_dtype(np.array(predictions)) else "\x00" # Truncate multi-step predictions if the (number of samples - rows_per_pred) is not a multiple of prediction_periods if (n_samples-rows_per_pred) % prediction_periods > 0: @@ -1342,11 +1353,21 @@ def sequence_predict(self, load_script=False, variant="predict"): else: y = np.array(predictions) - # Inverse transformations on the targets if required - if self.model.scale_target or self.model.make_stationary: + # Inverse transformations on the targets if required + if variant != 'internal' and (self.model.scale_target or self.model.make_stationary): # Apply the transformer to the test targets + placeholders = y[:self.placeholders] if prediction_periods > 1 or self.model.lag_target else [] + y = y if y_lags is None else np.append(y_lags, y[len(placeholders):]) y = self.model.target_transformer.inverse_transform(y) - + + # Replace lags used for making the series stationary with nulls in case of differencing + if self.model.make_stationary == 'difference': + null = np.NaN if is_numeric_dtype(np.array(predictions)) else "\x00" + y = np.append(np.array([null]*extra_lags), y[extra_lags:]) + + # Add back the placeholders for lag values + if len(placeholders) > 0: + y = np.append(placeholders, y) if variant == 'internal': return y @@ -1566,7 +1587,9 @@ def _set_params(self, estimator_args, scaler_args, execution_args, metric_args=N self.model.lags= None self.model.lag_target = False self.model.scale_target = False + self.model.scale_lag_target= True self.model.make_stationary = None + self.model.stationarity_lags = [1] self.model.using_keras = False self.model.current_sample_as_input = True self.model.prediction_periods = 1 @@ -1627,6 +1650,11 @@ def _set_params(self, estimator_args, scaler_args, execution_args, metric_args=N if 'scale_target' in execution_args: self.model.scale_target = 'true' == execution_args['scale_target'].lower() + # Scale lag values of the targets before fitting + # Even if scale_target is set to false, the lag values of targets being used as features can be scaled by setting this to true + if 'scale_lag_target' in execution_args: + self.model.scale_lag_target = 'true' == execution_args['scale_lag_target'].lower() + # Make the target series more stationary. This only applies to sequence prediction problems. # Valid values are 'log' in which case we apply a logarithm to the target values, # or 'difference' in which case we transform the targets into variance from the previous value. @@ -1634,6 +1662,12 @@ def _set_params(self, estimator_args, scaler_args, execution_args, metric_args=N if 'make_stationary' in execution_args: self.model.make_stationary = execution_args['make_stationary'].lower() + # Provide lags periods for differencing + # By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list of lags as a list. + # e.g. 'stationarity_lags=1;12|list|int' + if 'stationarity_lags' in execution_args: + self.model.stationarity_lags = utils.get_kwargs_by_type({'stationarity_lags': execution_args['stationarity_lags']})['stationarity_lags'] + # Specify if the current sample should be used as input to the model # This is to allow for models that only use lag observations to make future predictions if 'current_sample_as_input' in execution_args: @@ -1946,8 +1980,14 @@ def _add_lags(self, X, y=None, extrapolate=1, update_features_df=False): if update_features_df: # Check the target's data type dt = 'float' if is_numeric_dtype(y.iloc[:,0]) else 'str' - # Check if the target needs to be scaled - fs = 'scaling' if dt == 'float' and self.model.scale_target else 'none' + # Set the preprocessing feature strategy for the lag targets + if self.model.estimator_type == 'classifier': + fs = 'one hot encoding' + elif self.model.scale_lag_target and not self.model.scale_target: + fs = 'scaling' + else: + fs = 'none' + self.model.scale_lag_target # Update feature definitions for the model self.model.features_df.loc['previous_y'] = [self.model.name, 'previous_y', 'feature', dt, fs, ''] diff --git a/core/_utils.py b/core/_utils.py index 9cf79b9..928e4c6 100644 --- a/core/_utils.py +++ b/core/_utils.py @@ -437,6 +437,45 @@ def dict_to_sse_arg(d): return s[:-2] +def make_stationary(y, strategy='log', stationarity_lags=[1], array_like=True): + """ + Make a series stationary using a logarithm or differencing. + Valid values for the strategy parameter are 'log' or 'difference' + + The array_like parameter determines if y is expected to be multiple values or a single value. + Note that the differencing won't be done if array_like=False. + + By default the difference will be done with lag = 1. Alternate lags can be provided by passing a list of stationarity_lags. + e.g. stationarity_lags=[1, 12] + """ + + y_transform = y + + # Apply a logarithm to make the array stationary + if strategy == 'log': + y_transform = np.log(y) + + # Apply stationarity lags by differencing the array + elif strategy == 'difference' and array_like: + y_diff = y_transform.copy() + len_y = len(y_diff) + + for i in range(max(stationarity_lags), len_y): + for lag in stationarity_lags: + if isinstance(y_diff, (pd.Series, pd.DataFrame)): + y_diff.iloc[i] = y_diff.iloc[i] - y_transform.iloc[i - lag] + else: + y_diff[i] = y_diff[i] - y_transform[i - lag] + + # Remove targets with insufficient lag periods + # NOTE: The corresponding samples will need to be dropped at this function call's origin + if isinstance(y_diff, (pd.Series, pd.DataFrame)): + y_transform = y_diff.iloc[max(stationarity_lags):] + else: + y_transform = y_diff[max(stationarity_lags):] + + return y_transform + def add_lags(df, lag=1, extrapolate=1, dropna=True, suffix="t"): """ Take in a 2D DataFrame (n_samples by n_features) and create a new DataFrame with lag observations added to it. From c982a0234835927147d00a1e931f52c91acf7d52 Mon Sep 17 00:00:00 2001 From: Nabeel Date: Wed, 18 Mar 2020 15:46:50 +1100 Subject: [PATCH 2/5] Fixes in target transformation --- core/_machine_learning.py | 20 ++++++++++++-------- core/_sklearn.py | 12 +++++++++--- 2 files changed, 21 insertions(+), 11 deletions(-) diff --git a/core/_machine_learning.py b/core/_machine_learning.py index 28b961f..3f9532c 100644 --- a/core/_machine_learning.py +++ b/core/_machine_learning.py @@ -844,11 +844,11 @@ def transform(self, y, array_like=True): # Scale the targets using the previously fit scaler if self.scale: - y_transform = self.scaler_instance.transform(y) + y_transform_scaled = self.scaler_instance.transform(y_transform) - if isinstance(y, pd.DataFrame): + if isinstance(y_transform, pd.DataFrame): # The scaler returns a numpy array which needs to be converted back to a data frame - y_transform = pd.DataFrame(y_transform, columns=y.columns, index=y.index) + y_transform = pd.DataFrame(y_transform_scaled, columns=y_transform.columns, index=y_transform.index) if self.logfile is not None: self._print_log(1, data=y_transform, array_like=array_like) @@ -872,6 +872,8 @@ def inverse_transform(self, y_transform, array_like=True): For an inverse of differencing, y_transform needs to include sufficient actual lag values. """ + y = y_transform + # If inversing differencing, we assume sufficient lag values. # These should be actual values that have not been scaled previously. if self.make_stationary == 'difference' and array_like: @@ -884,16 +886,18 @@ def inverse_transform(self, y_transform, array_like=True): # Inverse the scaling for non-lag values if self.scale: - if isinstance(y_transform, pd.DataFrame): - y = self.scaler_instance.inverse_transform(np.reshape(y_transform.values, (-1, 1))) + if isinstance(y, pd.DataFrame): + y = self.scaler_instance.inverse_transform(np.reshape(y.values, (-1, 1))) # The scaler returns a numpy array which needs to be converted back to a data frame - y = pd.DataFrame(y, columns=y_transform.columns, index=y_transform.index) + y = pd.DataFrame(y, columns=y.columns, index=y.index) else: - y = self.scaler_instance.inverse_transform(np.reshape(y_transform, (-1, 1))) + y = self.scaler_instance.inverse_transform(np.reshape(y, (-1, 1))) + # Flatten the reshaping done above + y = y.ravel() # Apply an exponential to reverse the logarithm applied during transform if self.make_stationary == 'log': - y = np.exp(y_transform) + y = np.exp(y) # Reverse the differencing applied during transform # NOTE: y_transform will need to include actual lag values diff --git a/core/_sklearn.py b/core/_sklearn.py index 36a5df2..4997b67 100644 --- a/core/_sklearn.py +++ b/core/_sklearn.py @@ -916,7 +916,7 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Get predictions based on the samples if ordered_data: self.y_pred = self.sequence_predict(variant="internal") - + # Handle possible null values where a prediction could not be generated self.y_pred = self.y_pred[self.placeholders:] self.y_test = y_test_copy.iloc[self.placeholders+extra_lags:] @@ -925,6 +925,7 @@ def calculate_metrics(self, caller="external", ordered_data=False): if self.model.scale_target or self.model.make_stationary: # Apply the transformer to the test targets self.y_pred = self.y_pred if y_lags is None else np.append(y_lags, self.y_pred) + self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) # Remove lags used for making the series stationary in case of differencing if self.model.make_stationary == 'difference': @@ -1355,9 +1356,13 @@ def sequence_predict(self, load_script=False, variant="predict"): # Inverse transformations on the targets if required if variant != 'internal' and (self.model.scale_target or self.model.make_stationary): - # Apply the transformer to the test targets + # Take out placeholder values before inverse transform of targets placeholders = y[:self.placeholders] if prediction_periods > 1 or self.model.lag_target else [] - y = y if y_lags is None else np.append(y_lags, y[len(placeholders):]) + y = y if len(placeholders) == 0 else y[len(placeholders):] + # Add untransformed lag values for differencing if required + y = y if y_lags is None else np.append(y_lags, y) + + # Apply the transformer to the test targets y = self.model.target_transformer.inverse_transform(y) # Replace lags used for making the series stationary with nulls in case of differencing @@ -1368,6 +1373,7 @@ def sequence_predict(self, load_script=False, variant="predict"): # Add back the placeholders for lag values if len(placeholders) > 0: y = np.append(placeholders, y) + if variant == 'internal': return y From d91ca4086f982f16423ed478c609f8bc15d047fb Mon Sep 17 00:00:00 2001 From: Nabeel Date: Wed, 25 Mar 2020 20:47:05 +1100 Subject: [PATCH 3/5] Fixes for differencing targets --- core/_sklearn.py | 170 +++++++++++++------ docs/Sample-App-Forecasting-with-Sklearn.qvf | Bin 393216 -> 393216 bytes 2 files changed, 121 insertions(+), 49 deletions(-) diff --git a/core/_sklearn.py b/core/_sklearn.py index 4997b67..865ba91 100644 --- a/core/_sklearn.py +++ b/core/_sklearn.py @@ -897,18 +897,19 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Keep a copy of the y_test before any transformations y_test_copy = self.y_test.copy() - # Calculate if any extra lag periods are expected when making the target stationary using differencing - extra_lags = max(self.model.stationarity_lags) if self.model.lag_target and self.model.make_stationary=='difference' else 0 # Scale the targets and increase stationarity if required if self.model.scale_target or self.model.make_stationary: # If using differencing, we assume sufficient lag values for inversing the transformation later - y_lags = self.y_test.iloc[:extra_lags].values if self.model.make_stationary=='difference' else None + y_orig = self.y_test.values.ravel() if self.model.make_stationary=='difference' else None # Apply the transformer to the test targets self.y_test = self.model.target_transformer.transform(self.y_test) # Drop samples where self.y_test cannot be transformed due to insufficient lags self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):] + # TEMP LOG + sys.stdout.write("\n y_orig {}:\n{}\n\n".format(len(y_orig), y_orig)) + # Refresh the keras model to avoid tensorflow errors if self.model.using_keras: self._keras_refresh() @@ -917,19 +918,42 @@ def calculate_metrics(self, caller="external", ordered_data=False): if ordered_data: self.y_pred = self.sequence_predict(variant="internal") + # TEMP LOG + sys.stdout.write("\n self.y_pred received from sequence_predict {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) + # Handle possible null values where a prediction could not be generated - self.y_pred = self.y_pred[self.placeholders:] - self.y_test = y_test_copy.iloc[self.placeholders+extra_lags:] + self.y_pred = self.y_pred[self.rows_per_pred - self.first_pred_modifier:] + self.y_test = y_test_copy.iloc[-1*len(self.y_pred):] + + # TEMP LOG + sys.stdout.write("\nAfter removing placeholders self.y_test {}\n\n".format(len(self.y_test))) + sys.stdout.write("\nAfter removing placeholders self.y_pred {}\n\n".format(len(self.y_pred))) # Inverse transformations predictions if required if self.model.scale_target or self.model.make_stationary: - # Apply the transformer to the test targets - self.y_pred = self.y_pred if y_lags is None else np.append(y_lags, self.y_pred) + # Set up indices for differencing + end = self.placeholders + start = end - self.diff_lags + + # TEMP LOG + sys.stdout.write("\nself.placeholders: {}, self.rows_per_pred: {}, self.diff_lags: {}, start: {}, end: {}\n\n".format(self.placeholders, self.rows_per_pred, self.diff_lags, start, end)) + sys.stdout.write("\nAppending y_orig[start : end] {}:\n{}\n\n".format(len(y_orig[start : end]), y_orig[start : end])) + # Add lags for inversing differencing + self.y_pred = self.y_pred if y_orig is None else np.append(y_orig[start : end], self.y_pred) + + # TEMP LOG + sys.stdout.write("\nAfter appending y_orig self.y_pred {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) + + # Apply the transformer to the test targets self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) + + # TEMP LOG + sys.stdout.write("\nAfter inverse transform self.y_pred {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) + # Remove lags used for making the series stationary in case of differencing if self.model.make_stationary == 'difference': - self.y_pred = self.y_pred[extra_lags:] + self.y_pred = self.y_pred[self.diff_lags:] else: self.y_pred = self.model.pipe.predict(self.X_test) @@ -942,6 +966,10 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Flatten the y_test DataFrame self.y_test = self.y_test.values.ravel() + + # TEMP LOG + sys.stdout.write("\nFinal self.y_test {}:\n{}\n\n".format(len(self.y_test), self.y_test)) + sys.stdout.write("\nFinal self.y_pred {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) # Try getting the metric_args from the model try: @@ -1195,10 +1223,13 @@ def sequence_predict(self, load_script=False, variant="predict"): X = self.X_test.copy() y = self.y_test.copy() + # TEMP LOG + sys.stdout.write("\nlen X: {}, len y: {}\n\n".format(len(X), len(y))) + # Scale the targets and increase stationarity if required if variant != 'internal' and self.model.lag_target and (self.model.scale_target or self.model.make_stationary): - # If using differencing, we assume sufficient lag values for inversing the transformation later - y_lags = y.iloc[:max(self.model.stationarity_lags)].values if self.model.make_stationary=='difference' else None + # If using differencing, we retain original y values for inversing the transformation later + y_orig = y.values.ravel() if self.model.make_stationary=='difference' else None # Apply the transformer to the targets y = self.model.target_transformer.transform(y) # Drop samples where y cannot be transformed due to insufficient lags @@ -1207,21 +1238,21 @@ def sequence_predict(self, load_script=False, variant="predict"): # Set the number of periods to be predicted prediction_periods = self.model.prediction_periods # Set the number of rows required for one prediction - rows_per_pred = 1 - extra_lags = max(self.model.stationarity_lags) if self.model.lag_target and self.model.make_stationary=='difference' else 0 + self.rows_per_pred = 1 + self.diff_lags = max(self.model.stationarity_lags) if self.model.lag_target and self.model.make_stationary=='difference' else 0 + # Set property depending on whether the current sample will be included as an input, or if we only use lag observations for predictions + self.first_pred_modifier = 1 if self.model.current_sample_as_input else 0 # Check that the input data includes history to meet any lag calculation requirements if self.model.lags: - # Check if the current sample will be included as an input, or whether we only use lag observations for predictions - extrapolate = 1 if self.model.current_sample_as_input else 0 # An additional lag observation is needed if previous targets are being added to the features - rows_per_pred = self.model.lags+extrapolate+1 if self.model.lag_target else self.model.lags+extrapolate + self.rows_per_pred = self.model.lags+self.first_pred_modifier+1 if self.model.lag_target else self.model.lags+self.first_pred_modifier # If the target is being lagged and made stationary through differencing additional lag periods are required if self.model.lag_target and self.model.make_stationary=='difference': - extra_msg = " plus an additional {} periods for making the target stationary using differencing".format(extra_lags) + extra_msg = " plus an additional {} periods for making the target stationary using differencing".format(self.diff_lags) # For multi-step predictions we only expect lag values, not the current period's values - # rows_per_pred = rows_per_pred-1 if prediction_periods > 1 else rows_per_pred - assert len(X) >= rows_per_pred + extra_lags, "Insufficient input data as the model requires {} lag periods for each prediction".format(rows_per_pred) + extra_msg + # self.rows_per_pred = self.rows_per_pred-1 if prediction_periods > 1 else self.rows_per_pred + assert len(X) >= self.rows_per_pred + self.diff_lags, "Insufficient input data as the model requires {} lag periods for each prediction".format(self.rows_per_pred) + extra_msg if variant != 'internal': # Prepare the response DataFrame @@ -1245,25 +1276,25 @@ def sequence_predict(self, load_script=False, variant="predict"): # Check that we can generate 1 or more predictions of prediction_periods each n_samples = len(X) - assert (n_samples - rows_per_pred) >= prediction_periods, \ + assert (n_samples - self.rows_per_pred) >= prediction_periods, \ "Cannot generate predictions for {} periods with {} rows, with {} rows required for lag observations. You may need to provide more historical data or sufficient placeholder rows for future periods."\ - .format(prediction_periods, n_samples, rows_per_pred) + .format(prediction_periods, n_samples, self.rows_per_pred) # For multi-step predictions we can add lag observations up front as we only use actual values # i.e. We don't use predicted y values for further predictions if self.model.lags or self.model.lag_target: - X = self._add_lags(X, y=y, extrapolate=extrapolate) + X = self._add_lags(X, y=y, extrapolate=self.first_pred_modifier) # We start generating predictions from the first row as lags will already have been added to each sample start = 0 else: # We start generating predictions from the point where we will have sufficient lag observations - start = rows_per_pred + start = self.rows_per_pred if self.model.lag_target or prediction_periods > 1: # Get the predictions by walking forward over the data - for i in range(start, len(X), prediction_periods): - # For multi-step predictions we take in rows_per_pred rows of X to generate predictions for prediction_periods + for i in range(start, len(X) + self.first_pred_modifier, prediction_periods): + # For multi-step predictions we take in self.rows_per_pred rows of X to generate predictions for prediction_periods if prediction_periods > 1: batch_X = X.iloc[[i]] @@ -1280,10 +1311,10 @@ def sequence_predict(self, load_script=False, variant="predict"): probabilities += proba.tolist() # For walk forward predictions with lag targets we use each prediction as input to the next prediction, with X values avaialble for future periods. else: - batch_X = X.iloc[i-rows_per_pred : i] + batch_X = X.iloc[i-self.rows_per_pred : i] # Add lag observations - batch_y = y.iloc[i-rows_per_pred : i] - batch_X = self._add_lags(batch_X, y=batch_y, extrapolate=extrapolate) + batch_y = y.iloc[i-self.rows_per_pred : i] + batch_X = self._add_lags(batch_X, y=batch_y, extrapolate=self.first_pred_modifier) # Get the prediction. We only get a prediction for the last sample in the batch, the remaining samples only being used to add lags. pred = self.model.pipe.predict(batch_X.iloc[[-1],:]) @@ -1292,7 +1323,7 @@ def sequence_predict(self, load_script=False, variant="predict"): predictions.append(pred) # Add the prediction to y to be used as a lag target for the next prediction - y.iloc[i, 0] = pred + y.iloc[i - self.first_pred_modifier, 0] = pred # If probabilities need to be returned if get_proba: @@ -1301,7 +1332,7 @@ def sequence_predict(self, load_script=False, variant="predict"): else: # Add lag observations to the samples if required if self.model.lags: - X = self._add_lags(X, extrapolate=extrapolate) + X = self._add_lags(X, extrapolate=self.first_pred_modifier) # Get prediction for X predictions = self.model.pipe.predict(X) @@ -1313,15 +1344,20 @@ def sequence_predict(self, load_script=False, variant="predict"): # Set the number of placeholders needed in the response # These are samples for which predictions were not generated due to insufficient lag periods or for meeting multi-step prediction period requirements - self.placeholders = rows_per_pred + extra_lags + self.placeholders = self.rows_per_pred + self.diff_lags - self.first_pred_modifier + + # TEMP LOG + sys.stdout.write("\npredictions {}:\n{}\n\n".format(len(predictions),np.array(predictions).ravel())) + sys.stdout.write("\nlen X: {}, len y: {}\n\n".format(len(X), len(y))) + # Transform probabilities to a readable string if get_proba: # Add the required number of placeholders at the start of the response list y = ["\x00"] * self.placeholders - # Truncate multi-step predictions if the (number of samples - rows_per_pred) is not a multiple of prediction_periods - if prediction_periods > 1 and ((n_samples-rows_per_pred) % prediction_periods) > 0: - probabilities = probabilities[:-len(probabilities)+(n_samples-rows_per_pred)] + # Truncate multi-step predictions if the (number of samples - self.rows_per_pred) is not a multiple of prediction_periods + if prediction_periods > 1 and ((n_samples-self.rows_per_pred) % prediction_periods) > 0: + probabilities = probabilities[:-len(probabilities)+(n_samples-self.rows_per_pred)] for a in probabilities: s = "" @@ -1337,18 +1373,28 @@ def sequence_predict(self, load_script=False, variant="predict"): # Set the value to use for nulls null = np.NaN if is_numeric_dtype(np.array(predictions)) else "\x00" - # Truncate multi-step predictions if the (number of samples - rows_per_pred) is not a multiple of prediction_periods - if (n_samples-rows_per_pred) % prediction_periods > 0: - predictions = predictions[:-len(predictions)+(n_samples-rows_per_pred)] - + # TEMP LOG + sys.stdout.write("\npredictions len: {}, n_samples: {}, prediction_periods: {}\n\n".format(len(predictions),n_samples,prediction_periods)) + sys.stdout.write("\nself.placeholders: {}, self.rows_per_pred: {}, self.diff_lags: {}\n\n".format(self.placeholders, self.rows_per_pred, self.diff_lags)) + + # Truncate multi-step predictions if the (number of samples - self.placeholders) is not a multiple of prediction_periods + if (n_samples-self.rows_per_pred) % prediction_periods > 0: + predictions = predictions[:-len(predictions)+(n_samples-self.rows_per_pred)] + + # TEMP LOG + sys.stdout.write("\npredictions len after truncation: {}\n\n".format(len(predictions))) + # Add null values at the start of the response list to match the cardinality of the input from Qlik - y = np.array(([null] * self.placeholders) + predictions) + y = np.array(([null] * (self.rows_per_pred - self.first_pred_modifier)) + predictions) + + # TEMP LOG + sys.stdout.write("\ny len: {}\n\n".format(len(y))) elif self.model.lag_target: # Remove actual values for which we did not generate predictions due to insufficient lags if is_numeric_dtype(y.iloc[:, 0].dtype): - y.iloc[:self.placeholders, 0] = np.NaN + y.iloc[:self.placeholders - self.first_pred_modifier, 0] = np.NaN else: - y.iloc[:self.placeholders, 0] = "\x00" + y.iloc[:self.placeholders - self.first_pred_modifier, 0] = "\x00" # Flatten y to the expected 1D shape y = y.values.ravel() else: @@ -1357,22 +1403,48 @@ def sequence_predict(self, load_script=False, variant="predict"): # Inverse transformations on the targets if required if variant != 'internal' and (self.model.scale_target or self.model.make_stationary): # Take out placeholder values before inverse transform of targets - placeholders = y[:self.placeholders] if prediction_periods > 1 or self.model.lag_target else [] - y = y if len(placeholders) == 0 else y[len(placeholders):] + null_values = y[:self.rows_per_pred - self.first_pred_modifier] if prediction_periods > 1 or self.model.lag_target else [] + # Add placeholders for samples removed during differencing + if self.model.make_stationary=='difference': + null_values = np.append(null_values, np.repeat(null_values[0], self.diff_lags)) + y = y if len(null_values) == 0 else y[-len(predictions):] # Add untransformed lag values for differencing if required - y = y if y_lags is None else np.append(y_lags, y) - + end = self.placeholders + start = end - self.diff_lags + + # TEMP LOG + sys.stdout.write("\nself.placeholders: {}, self.rows_per_pred: {}, self.diff_lags: {}, start: {}, end: {}\n\n".format(self.placeholders, self.rows_per_pred, self.diff_lags, start, end)) + sys.stdout.write("\nAppending y_orig[start : end] {}:\n{}\n\n".format(len(y_orig[start : end]), y_orig[start : end])) + + # TEMP LOG + sys.stdout.write("\ny before append and inverse transform {}:\n{}\n\n".format(len(y), y)) + + y = y if y_orig is None else np.append(y_orig[start : end], y) + + # TEMP LOG + sys.stdout.write("\ny after append, before inverse transform {}:\n{}\n\n".format(len(y), y)) + # Apply the transformer to the test targets y = self.model.target_transformer.inverse_transform(y) + + # TEMP LOG + sys.stdout.write("\ny after inverse transform {}:\n{}\n\n".format(len(y), y)) - # Replace lags used for making the series stationary with nulls in case of differencing + # Remove lags used for making the series stationary in case of differencing if self.model.make_stationary == 'difference': - null = np.NaN if is_numeric_dtype(np.array(predictions)) else "\x00" - y = np.append(np.array([null]*extra_lags), y[extra_lags:]) + y = y[self.diff_lags:] + + # Replace lags used for making the series stationary with nulls in case of differencing + # if self.model.make_stationary == 'difference': + #null = np.NaN if is_numeric_dtype(np.array(predictions)) else "\x00" + # y = np.append(np.array([null]*self.diff_lags), y[self.diff_lags:]) # Add back the placeholders for lag values - if len(placeholders) > 0: - y = np.append(placeholders, y) + if len(null_values) > 0: + y = np.append(null_values, y) + + # TEMP LOG + sys.stdout.write("\nlen(null_values): {}, len(X), len(y): {}, len(self.response): {}\n\n".format(len(null_values), len(X), len(y), len(self.response))) if variant == 'internal': return y diff --git 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insertions(+), 60 deletions(-) diff --git a/core/_sklearn.py b/core/_sklearn.py index 865ba91..d2d7d81 100644 --- a/core/_sklearn.py +++ b/core/_sklearn.py @@ -907,9 +907,6 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Drop samples where self.y_test cannot be transformed due to insufficient lags self.X_test = self.X_test.iloc[len(self.X_test)-len(self.y_test):] - # TEMP LOG - sys.stdout.write("\n y_orig {}:\n{}\n\n".format(len(y_orig), y_orig)) - # Refresh the keras model to avoid tensorflow errors if self.model.using_keras: self._keras_refresh() @@ -918,39 +915,22 @@ def calculate_metrics(self, caller="external", ordered_data=False): if ordered_data: self.y_pred = self.sequence_predict(variant="internal") - # TEMP LOG - sys.stdout.write("\n self.y_pred received from sequence_predict {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) - # Handle possible null values where a prediction could not be generated self.y_pred = self.y_pred[self.rows_per_pred - self.first_pred_modifier:] self.y_test = y_test_copy.iloc[-1*len(self.y_pred):] - # TEMP LOG - sys.stdout.write("\nAfter removing placeholders self.y_test {}\n\n".format(len(self.y_test))) - sys.stdout.write("\nAfter removing placeholders self.y_pred {}\n\n".format(len(self.y_pred))) - # Inverse transformations predictions if required if self.model.scale_target or self.model.make_stationary: # Set up indices for differencing end = self.placeholders start = end - self.diff_lags - # TEMP LOG - sys.stdout.write("\nself.placeholders: {}, self.rows_per_pred: {}, self.diff_lags: {}, start: {}, end: {}\n\n".format(self.placeholders, self.rows_per_pred, self.diff_lags, start, end)) - sys.stdout.write("\nAppending y_orig[start : end] {}:\n{}\n\n".format(len(y_orig[start : end]), y_orig[start : end])) - # Add lags for inversing differencing self.y_pred = self.y_pred if y_orig is None else np.append(y_orig[start : end], self.y_pred) - # TEMP LOG - sys.stdout.write("\nAfter appending y_orig self.y_pred {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) - # Apply the transformer to the test targets self.y_pred = self.model.target_transformer.inverse_transform(self.y_pred) - # TEMP LOG - sys.stdout.write("\nAfter inverse transform self.y_pred {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) - # Remove lags used for making the series stationary in case of differencing if self.model.make_stationary == 'difference': self.y_pred = self.y_pred[self.diff_lags:] @@ -967,10 +947,6 @@ def calculate_metrics(self, caller="external", ordered_data=False): # Flatten the y_test DataFrame self.y_test = self.y_test.values.ravel() - # TEMP LOG - sys.stdout.write("\nFinal self.y_test {}:\n{}\n\n".format(len(self.y_test), self.y_test)) - sys.stdout.write("\nFinal self.y_pred {}:\n{}\n\n".format(len(self.y_pred), self.y_pred)) - # Try getting the metric_args from the model try: metric_args = self.model.metric_args @@ -1223,10 +1199,7 @@ def sequence_predict(self, load_script=False, variant="predict"): X = self.X_test.copy() y = self.y_test.copy() - # TEMP LOG - sys.stdout.write("\nlen X: {}, len y: {}\n\n".format(len(X), len(y))) - - # Scale the targets and increase stationarity if required + # Scale the targets and increase stationarity if required if variant != 'internal' and self.model.lag_target and (self.model.scale_target or self.model.make_stationary): # If using differencing, we retain original y values for inversing the transformation later y_orig = y.values.ravel() if self.model.make_stationary=='difference' else None @@ -1346,11 +1319,7 @@ def sequence_predict(self, load_script=False, variant="predict"): # These are samples for which predictions were not generated due to insufficient lag periods or for meeting multi-step prediction period requirements self.placeholders = self.rows_per_pred + self.diff_lags - self.first_pred_modifier - # TEMP LOG - sys.stdout.write("\npredictions {}:\n{}\n\n".format(len(predictions),np.array(predictions).ravel())) - sys.stdout.write("\nlen X: {}, len y: {}\n\n".format(len(X), len(y))) - - # Transform probabilities to a readable string + # Transform probabilities to a readable string if get_proba: # Add the required number of placeholders at the start of the response list y = ["\x00"] * self.placeholders @@ -1373,22 +1342,12 @@ def sequence_predict(self, load_script=False, variant="predict"): # Set the value to use for nulls null = np.NaN if is_numeric_dtype(np.array(predictions)) else "\x00" - # TEMP LOG - sys.stdout.write("\npredictions len: {}, n_samples: {}, prediction_periods: {}\n\n".format(len(predictions),n_samples,prediction_periods)) - sys.stdout.write("\nself.placeholders: {}, self.rows_per_pred: {}, self.diff_lags: {}\n\n".format(self.placeholders, self.rows_per_pred, self.diff_lags)) - # Truncate multi-step predictions if the (number of samples - self.placeholders) is not a multiple of prediction_periods if (n_samples-self.rows_per_pred) % prediction_periods > 0: predictions = predictions[:-len(predictions)+(n_samples-self.rows_per_pred)] - # TEMP LOG - sys.stdout.write("\npredictions len after truncation: {}\n\n".format(len(predictions))) - # Add null values at the start of the response list to match the cardinality of the input from Qlik y = np.array(([null] * (self.rows_per_pred - self.first_pred_modifier)) + predictions) - - # TEMP LOG - sys.stdout.write("\ny len: {}\n\n".format(len(y))) elif self.model.lag_target: # Remove actual values for which we did not generate predictions due to insufficient lags if is_numeric_dtype(y.iloc[:, 0].dtype): @@ -1411,25 +1370,11 @@ def sequence_predict(self, load_script=False, variant="predict"): # Add untransformed lag values for differencing if required end = self.placeholders start = end - self.diff_lags - - # TEMP LOG - sys.stdout.write("\nself.placeholders: {}, self.rows_per_pred: {}, self.diff_lags: {}, start: {}, end: {}\n\n".format(self.placeholders, self.rows_per_pred, self.diff_lags, start, end)) - sys.stdout.write("\nAppending y_orig[start : end] {}:\n{}\n\n".format(len(y_orig[start : end]), y_orig[start : end])) - - # TEMP LOG - sys.stdout.write("\ny before append and inverse transform {}:\n{}\n\n".format(len(y), y)) - y = y if y_orig is None else np.append(y_orig[start : end], y) - # TEMP LOG - sys.stdout.write("\ny after append, before inverse transform {}:\n{}\n\n".format(len(y), y)) - # Apply the transformer to the test targets y = self.model.target_transformer.inverse_transform(y) - # TEMP LOG - sys.stdout.write("\ny after inverse transform {}:\n{}\n\n".format(len(y), y)) - # Remove lags used for making the series stationary in case of differencing if self.model.make_stationary == 'difference': y = y[self.diff_lags:] @@ -1442,9 +1387,6 @@ def sequence_predict(self, load_script=False, variant="predict"): # Add back the placeholders for lag values if len(null_values) > 0: y = np.append(null_values, y) - - # TEMP LOG - sys.stdout.write("\nlen(null_values): {}, len(X), len(y): {}, len(self.response): {}\n\n".format(len(null_values), len(X), len(y), len(self.response))) if variant == 'internal': return y From 402cfe12ec1c00a29f37011fddd652b03843e7ca Mon Sep 17 00:00:00 2001 From: Nabeel Date: Thu, 26 Mar 2020 14:34:06 +1100 Subject: [PATCH 5/5] Dockerfile v.7.1 --- docker/Dockerfile v.7.1 | 14 ++++++++++++++ 1 file changed, 14 insertions(+) create mode 100644 docker/Dockerfile v.7.1 diff --git a/docker/Dockerfile v.7.1 b/docker/Dockerfile v.7.1 new file mode 100644 index 0000000..afba5f0 --- /dev/null +++ b/docker/Dockerfile v.7.1 @@ -0,0 +1,14 @@ +# Use the previous version of qlik-py-tools as a parent image +FROM nabeeloz/qlik-py-tools:7.0 + +# Set the working directory to /qlik-py-tools/core +WORKDIR /qlik-py-tools/core + +# Copy all files from the core subdirectory into the container +COPY ./core/* /qlik-py-tools/core/ + +# Make ports 80 and 50055 available to the world outside this container +EXPOSE 80 50055 + +# Run __main__.py when the container launches +CMD ["python", "__main__.py"] \ No newline at end of file