Update docs

Author: riibeirogabriel

README.md

@@ -23,182 +23,8 @@ Each functionality in learningOrchestra is contained in its own class. Check the
 
 # Example
 
-Shown below is an example usage of learning-orchestra-client using the [Titanic Dataset](https://www.kaggle.com/c/titanic/overview):
+* [Here](examples/titanic.py) has an example using the [Titanic Dataset](https://www.kaggle.com/c/titanic/overview):
+* [Here](examples/sentiment_analysis.py) has an example using the [Sentiment Analysis On IMDb reviews](https://www.kaggle.com/avnika22/imdb-perform-sentiment-analysis-with-scikit-learn):
+* [Here](examples/mnist.py) has an example using the [MNIST Dataset](http://yann.lecun.com/exdb/mnist/):
 
-```python
-from learning_orchestra_client import (
-    dataset,
-    builder,
-    transform,
-)
 
-cluster_ip = "34.95.187.26"
-
-
-dataset = Dataset(cluster_ip)
-
-print(dataset.insert_dataset_sync(
-    "titanic_training",
-    "https://filebin.net/rpfdy8clm5984a4c/titanic_training.csv?t=gcnjz1yo"))
-print(dataset.insert_dataset_sync(
-    "titanic_testing",
-    "https://filebin.net/mguee52ke97k0x9h/titanic_testing.csv?t=ub4nc1rc"))
-
-print(dataset.search_all_datasets())
-
-
-projection = Projection(cluster_ip)
-required_columns = [
-    "PassengerId",
-    "Pclass",
-    "Age",
-    "SibSp",
-    "Parch",
-    "Fare",
-    "Name",
-    "Sex",
-    "Embarked",
-    "Survived"
-    ]
-print(projection.insert_dataset_attributes_sync(
-        "titanic_training",
-        "titanic_training_projection",
-        required_columns))
-
-required_columns.remove("Survived")
-
-print(projection.insert_dataset_attributes_sync(
-    "titanic_testing",
-    "titanic_testing_projection",
-    required_columns))
-
-
-data_type_handler = DataType(cluster_ip)
-type_fields = {
-    "Age": "number",
-    "Fare": "number",
-    "Parch": "number",
-    "PassengerId": "number",
-    "Pclass": "number",
-    "SibSp": "number"
-}
-
-print(data_type_handler.update_dataset_types(
-    "titanic_testing_projection",
-    type_fields))
-
-type_fields["Survived"] = "number"
-
-print(data_type_handler.update_dataset_types(
-    "titanic_training_projection",
-    type_fields))
-
-
-modeling_code = '''
-from pyspark.ml import Pipeline
-from pyspark.sql.functions import (
-    mean, col, split,
-    regexp_extract, when, lit)
-
-from pyspark.ml.feature import (
-    VectorAssembler,
-    StringIndexer
-)
-
-TRAINING_DF_INDEX = 0
-TESTING_DF_INDEX = 1
-
-training_df = training_df.withColumnRenamed('Survived', 'label')
-testing_df = testing_df.withColumn('label', lit(0))
-datasets_list = [training_df, testing_df]
-
-for index, dataset in enumerate(datasets_list):
-    dataset = dataset.withColumn(
-        "Initial",
-        regexp_extract(col("Name"), "([A-Za-z]+)\.", 1))
-    datasets_list[index] = dataset
-
-misspelled_initials = [
-    'Mlle', 'Mme', 'Ms', 'Dr',
-    'Major', 'Lady', 'Countess',
-    'Jonkheer', 'Col', 'Rev',
-    'Capt', 'Sir', 'Don'
-]
-correct_initials = [
-    'Miss', 'Miss', 'Miss', 'Mr',
-    'Mr', 'Mrs', 'Mrs',
-    'Other', 'Other', 'Other',
-    'Mr', 'Mr', 'Mr'
-]
-for index, dataset in enumerate(datasets_list):
-    dataset = dataset.replace(misspelled_initials, correct_initials)
-    datasets_list[index] = dataset
-
-
-initials_age = {"Miss": 22,
-                "Other": 46,
-                "Master": 5,
-                "Mr": 33,
-                "Mrs": 36}
-for index, dataset in enumerate(datasets_list):
-    for initial, initial_age in initials_age.items():
-        dataset = dataset.withColumn(
-            "Age",
-            when((dataset["Initial"] == initial) &
-                 (dataset["Age"].isNull()), initial_age).otherwise(
-                    dataset["Age"]))
-        datasets_list[index] = dataset
-
-
-for index, dataset in enumerate(datasets_list):
-    dataset = dataset.na.fill({"Embarked": 'S'})
-    datasets_list[index] = dataset
-
-
-for index, dataset in enumerate(datasets_list):
-    dataset = dataset.withColumn("Family_Size", col('SibSp')+col('Parch'))
-    dataset = dataset.withColumn('Alone', lit(0))
-    dataset = dataset.withColumn(
-        "Alone",
-        when(dataset["Family_Size"] == 0, 1).otherwise(dataset["Alone"]))
-    datasets_list[index] = dataset
-
-
-text_fields = ["Sex", "Embarked", "Initial"]
-for column in text_fields:
-    for index, dataset in enumerate(datasets_list):
-        dataset = StringIndexer(
-            inputCol=column, outputCol=column+"_index").\
-                fit(dataset).\
-                transform(dataset)
-        datasets_list[index] = dataset
-
-
-non_required_columns = ["Name", "Embarked", "Sex", "Initial"]
-for index, dataset in enumerate(datasets_list):
-    dataset = dataset.drop(*non_required_columns)
-    datasets_list[index] = dataset
-
-
-training_df = datasets_list[TRAINING_DF_INDEX]
-testing_df = datasets_list[TESTING_DF_INDEX]
-
-assembler = VectorAssembler(
-    inputCols=training_df.columns[:],
-    outputCol="features")
-assembler.setHandleInvalid('skip')
-
-features_training = assembler.transform(training_df)
-(features_training, features_evaluation) =\
-    features_training.randomSplit([0.8, 0.2], seed=33)
-features_testing = assembler.transform(testing_df)
-'''
-
-builder = Builder(cluster_ip)
-
-print(builder.run_builder_sync(
-    "titanic_training_projection",
-    "titanic_testing_projection",
-    modeling_code,
-    ["lr", "dt", "gb", "rf", "nb"]))
-```