fixed pre commit hooks

examples/indian-diabetes-example/neural-network.yaml

@@ -1,13 +1,13 @@
+
+
 dataset:
     type: csv
-    random_numbers:
-        generate_reproducible: true
-        seed: 101
+
 # model definition
 model:
     type: classification
     algorithm: NeuralNetwork
 
-# target you want to predict
+# target(s) you want to predict
 target:
     - sick

examples/indian-diabetes-example/random-forest.yaml

@@ -1,33 +1,34 @@
 
 
-# dataset operations
 dataset:
     type: csv
     random_numbers:
         generate_reproducible: true
         seed: 42
-    split:  # split options
-        test_size: 0.2  # 0.2 means 20% for the test data, so 80% are automatically for training
-        shuffle: True   # whether to shuffle the data before/while splitting
+    split:
+        test_size: 0.2
+        shuffle: True
 
-    preprocess: # preprocessing options
-        missing_values: mean    # other possible values: [drop, median, most_frequent, constant] check the docs for more
+    preprocess:
+        missing_values: mean
         encoding:
-            type: oneHotEncoding  # other possible values: [labelEncoding]
-        scale:  # scaling options
-            method: standard    # standardization will scale values to have a 0 mean and 1 standard deviation  | you can also try minmax
-            target: inputs  # scale inputs. | other possible values: [outputs, all] # if you choose all then all values in the dataset will be scaled
+            type: oneHotEncoding
+        scale:
+            method: standard
+            target: inputs
 
-
-# model definition
 model:
     type: classification
     algorithm: RandomForest
     arguments:
         n_estimators: 100
         max_depth: 30
+    hyperparameter_search:
+    method: random_search
+    parameter_grid:
+        max_depth: [6, 10]
+        n_estimators: [100, 300]
+        max_features: [auto, sqrt]
 
-
-# target you want to predict
 target:
     - sick

igel/__init__.py

@@ -10,7 +10,7 @@
 try:
     __version__ = version(__name__)
 except PackageNotFoundError:
-    __version__ = "0.5.0"
+    __version__ = "0.7.0"
 
 
 __author__ = "Nidhal Baccouri"

igel/__main__.py

@@ -8,7 +8,6 @@
 import igel
 import pandas as pd
 from igel import Igel, metrics_dict
-from igel.auto import IgelCNN
 from igel.constants import Constants
 from igel.servers import fastapi_server
 from igel.utils import print_models_overview, show_model_info, tableize
@@ -73,29 +72,6 @@ def fit(data_path: str, yaml_path: str) -> None:
     Igel(cmd="fit", data_path=data_path, yaml_path=yaml_path)
 
 
-@cli.command(context_settings=CONTEXT_SETTINGS)
-@click.option(
-    "--data_path", "-dp", required=True, help="Path to your training dataset"
-)
-@click.option(
-    "--task",
-    "-t",
-    required=False,
-    help="task you want to run. This refers to the goal you want to achieve (e.g ImageClassification)",
-)
-@click.option(
-    "--yaml_path",
-    "-yml",
-    required=False,
-    help="Path to your igel configuration file (yaml or json file)",
-)
-def auto_train(data_path: str, task: str, yaml_path: str) -> None:
-    """
-    Automatically search for and train a suitable deep neural network for a task
-    """
-    IgelCNN(cmd="train", data_path=data_path, task=task, yaml_path=yaml_path)
-
-
 @cli.command(context_settings=CONTEXT_SETTINGS)
 @click.option(
     "--data_path", "-dp", required=True, help="Path to your evaluation dataset"
@@ -107,17 +83,6 @@ def evaluate(data_path: str) -> None:
     Igel(cmd="evaluate", data_path=data_path)
 
 
-@cli.command(context_settings=CONTEXT_SETTINGS)
-@click.option(
-    "--data_path", "-dp", required=True, help="Path to your evaluation dataset"
-)
-def auto_evaluate(data_path: str) -> None:
-    """
-    Evaluate the performance of an existing machine learning model
-    """
-    IgelCNN(cmd="evaluate", data_path=data_path)
-
-
 @cli.command(context_settings=CONTEXT_SETTINGS)
 @click.option("--data_path", "-dp", required=True, help="Path to your dataset")
 def predict(data_path: str) -> None:
@@ -127,15 +92,6 @@ def predict(data_path: str) -> None:
     Igel(cmd="predict", data_path=data_path)
 
 
-@cli.command(context_settings=CONTEXT_SETTINGS)
-@click.option("--data_path", "-dp", required=True, help="Path to your dataset")
-def auto_predict(data_path: str) -> None:
-    """
-    Use an existing machine learning model to generate predictions
-    """
-    IgelCNN(cmd="predict", data_path=data_path)
-
-
 @cli.command(context_settings=CONTEXT_SETTINGS)
 @click.option(
     "--data_paths",
@@ -161,31 +117,6 @@ def experiment(data_paths: str, yaml_path: str) -> None:
     Igel(cmd="predict", data_path=pred_data_path)
 
 
-@cli.command(context_settings=CONTEXT_SETTINGS)
-@click.option(
-    "--data_paths",
-    "-DP",
-    required=True,
-    help="Path to your datasets as string separated by space",
-)
-@click.option(
-    "--yaml_path",
-    "-yml",
-    required=True,
-    help="Path to your igel configuration file (yaml or json file)",
-)
-def auto_experiment(data_paths: str, yaml_path: str) -> None:
-    """
-    train, evaluate and use pre-trained model for predictions in one command
-    """
-    train_data_path, eval_data_path, pred_data_path = data_paths.strip().split(
-        " "
-    )
-    IgelCNN(cmd="train", data_path=train_data_path, yaml_path=yaml_path)
-    IgelCNN(cmd="evaluate", data_path=eval_data_path)
-    IgelCNN(cmd="predict", data_path=pred_data_path)
-
-
 @cli.command(context_settings=CONTEXT_SETTINGS)
 @click.option(
     "--model_results_dir",

setup.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.1
+current_version = 0.7.0
 
 [darglint]
 strictness = long