When working with machine learning data, splitting into a "train", "dev" (or validation) and "test") set is important. Models use train data to learn representations and update their parameters; dev or validation data is reserved to see how the model may perform on unknown predictions. While it may not be explicitly trained on, it can be used as a stopping criteria, for hyper-parameter tuning, or as a simple sanity check. Lastly, test data is always reserved, hidden from the model, as a final pass to see what models perform best.
Lightwood supports a variety of encoders (Feature engineering procedures) and mixers (predictor algorithms that go from feature vectors to the target). Given the diversity of algorithms, it is appropriate to split data into these three categories when preparing encoders or fitting mixers.
Our default approach stratifies labeled data to ensure your train, validation, and test sets are equally represented in all classes. However, in many instances you may want a custom technique to build your own splits. We've included the splitter
functionality (default found in lightwood.data.splitter
) to enable you to build your own.
In the following problem, we shall work with a Kaggle dataset around credit card fraud (found here). Fraud detection is difficult because the events we are interested in catching are thankfully rare events. Because of that, there is a large imbalance of classes (in fact, in this dataset, less than 1% of the data are the rare-event).
In a supervised technique, we may want to ensure our training data sees the rare event of interest. A random shuffle could potentially miss rare events. We will implement SMOTE to increase the number of positive classes in our training data.
Let's get started!
import numpy as np
import pandas as pd
import torch
import nltk
import matplotlib.pyplot as plt
import os
import sys
# Lightwood modules
import lightwood as lw
from lightwood import ProblemDefinition, \
JsonAI, \
json_ai_from_problem, \
code_from_json_ai, \
predictor_from_code
import imblearn # Vers 0.5.0 minimum requirement
Lightwood works with pandas
DataFrames. We can use pandas to load our data. Please download the dataset from the above link and place it in a folder called data/
where this notebook is located.
# Load the data
ddir = "data/"
filename = os.path.join(ddir, "creditcard.csv.zip")
data = pd.read_csv(filename)
data.head()
Time | V1 | V2 | V3 | V4 | V5 | V6 | V7 | V8 | V9 | ... | V21 | V22 | V23 | V24 | V25 | V26 | V27 | V28 | Amount | Class | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.0 | -1.359807 | -0.072781 | 2.536347 | 1.378155 | -0.338321 | 0.462388 | 0.239599 | 0.098698 | 0.363787 | ... | -0.018307 | 0.277838 | -0.110474 | 0.066928 | 0.128539 | -0.189115 | 0.133558 | -0.021053 | 149.62 | 0 |
1 | 0.0 | 1.191857 | 0.266151 | 0.166480 | 0.448154 | 0.060018 | -0.082361 | -0.078803 | 0.085102 | -0.255425 | ... | -0.225775 | -0.638672 | 0.101288 | -0.339846 | 0.167170 | 0.125895 | -0.008983 | 0.014724 | 2.69 | 0 |
2 | 1.0 | -1.358354 | -1.340163 | 1.773209 | 0.379780 | -0.503198 | 1.800499 | 0.791461 | 0.247676 | -1.514654 | ... | 0.247998 | 0.771679 | 0.909412 | -0.689281 | -0.327642 | -0.139097 | -0.055353 | -0.059752 | 378.66 | 0 |
3 | 1.0 | -0.966272 | -0.185226 | 1.792993 | -0.863291 | -0.010309 | 1.247203 | 0.237609 | 0.377436 | -1.387024 | ... | -0.108300 | 0.005274 | -0.190321 | -1.175575 | 0.647376 | -0.221929 | 0.062723 | 0.061458 | 123.50 | 0 |
4 | 2.0 | -1.158233 | 0.877737 | 1.548718 | 0.403034 | -0.407193 | 0.095921 | 0.592941 | -0.270533 | 0.817739 | ... | -0.009431 | 0.798278 | -0.137458 | 0.141267 | -0.206010 | 0.502292 | 0.219422 | 0.215153 | 69.99 | 0 |
5 rows × 31 columns
We see 31 columns, most of these columns appear numerical. Due to confidentiality reasons, the Kaggle dataset mentions that the columns labeled $V_i$ indicate principle components (PCs) from a PCA analysis of the original data from the credit card company. There is also a "Time" and "Amount", two original features that remained. The time references time after the first transaction in the dataset, and amount is how much money was considered in the transaction.
You can also see a heavy imbalance in the two classes below:
f = plt.figure()
ax = f.add_subplot(1,1,1)
ax.hist(data['Class'], bins = [-0.1, 0.1, 0.9, 1.1], log=True)
ax.set_ylabel("Log Counts")
ax.set_xticks([0, 1])
ax.set_xticklabels(["0", "1"])
ax.set_xlabel("Class")
ax.set_title("Distribution of Classes")
Text(0.5, 1.0, 'Distribution of Classes')
We will now create JSON-AI syntax for our problem based on its specifications. We can do so by setting up a ProblemDefinition
. The ProblemDefinition
allows us to specify the target, the column we intend to predict, along with other details.
The end goal of JSON-AI is to provide *a set of instructions on how to compile a machine learning pipeline.
Our target here is called "Class", which indicates "0" for no fraud and "1" for fraud. We'll generate the JSON-AI with the minimal syntax:
# Setup the problem definition
problem_definition = {
'target': 'Class',
}
# Generate the j{ai}son syntax
default_json = json_ai_from_problem(data, problem_definition)
INFO:lightwood-51500:Dropping features: [] INFO:lightwood-51500:Analyzing a sample of 18424 INFO:lightwood-51500:from a total population of 284807, this is equivalent to 6.5% of your data. INFO:lightwood-51500:Using 15 processes to deduct types. INFO:lightwood-51500:Infering type for: Time INFO:lightwood-51500:Infering type for: V1 INFO:lightwood-51500:Infering type for: V2 INFO:lightwood-51500:Infering type for: V3 INFO:lightwood-51500:Infering type for: V4 INFO:lightwood-51500:Infering type for: V5 INFO:lightwood-51500:Infering type for: V6 INFO:lightwood-51500:Infering type for: V7 INFO:lightwood-51500:Infering type for: V8 INFO:lightwood-51500:Infering type for: V9 INFO:lightwood-51500:Infering type for: V10 INFO:lightwood-51500:Infering type for: V11 INFO:lightwood-51500:Infering type for: V12 INFO:lightwood-51500:Infering type for: V13 INFO:lightwood-51500:Infering type for: V14 INFO:lightwood-51500:Column Time has data type integer INFO:lightwood-51500:Infering type for: V15 INFO:lightwood-51500:Column V4 has data type float INFO:lightwood-51500:Infering type for: V16 INFO:lightwood-51500:Column V2 has data type float INFO:lightwood-51500:Infering type for: V17 INFO:lightwood-51500:Column V3 has data type float INFO:lightwood-51500:Column V1 has data type float INFO:lightwood-51500:Infering type for: V18 INFO:lightwood-51500:Infering type for: V19 INFO:lightwood-51500:Column V6 has data type float INFO:lightwood-51500:Column V5 has data type float INFO:lightwood-51500:Infering type for: V20 INFO:lightwood-51500:Column V7 has data type float INFO:lightwood-51500:Infering type for: V21 INFO:lightwood-51500:Column V8 has data type float INFO:lightwood-51500:Infering type for: V22 INFO:lightwood-51500:Infering type for: V23 INFO:lightwood-51500:Column V9 has data type float INFO:lightwood-51500:Infering type for: V24 INFO:lightwood-51500:Column V10 has data type float INFO:lightwood-51500:Column V13 has data type float INFO:lightwood-51500:Column V12 has data type float INFO:lightwood-51500:Infering type for: V25 INFO:lightwood-51500:Column V11 has data type float INFO:lightwood-51500:Infering type for: V26 INFO:lightwood-51500:Column V14 has data type float INFO:lightwood-51500:Infering type for: V28 INFO:lightwood-51500:Infering type for: V27 INFO:lightwood-51500:Infering type for: Amount INFO:lightwood-51500:Column V15 has data type float INFO:lightwood-51500:Infering type for: Class INFO:lightwood-51500:Column V16 has data type float INFO:lightwood-51500:Column V17 has data type float INFO:lightwood-51500:Column Class has data type binary INFO:lightwood-51500:Column Amount has data type float INFO:lightwood-51500:Column V23 has data type float INFO:lightwood-51500:Column V18 has data type float INFO:lightwood-51500:Column V19 has data type float INFO:lightwood-51500:Column V20 has data type float INFO:lightwood-51500:Column V28 has data type float INFO:lightwood-51500:Column V21 has data type float INFO:lightwood-51500:Column V22 has data type float INFO:lightwood-51500:Column V26 has data type float INFO:lightwood-51500:Column V24 has data type float INFO:lightwood-51500:Column V25 has data type float INFO:lightwood-51500:Column V27 has data type float INFO:lightwood-51500:Starting statistical analysis INFO:lightwood-51500:Finished statistical analysis
MyCustomCleaner.py MyCustomCleaner MyCustomSplitter.py MyCustomSplitter
Lightwood looks at each of the many columns and indicates they are mostly float, with exception of "Class" which is binary.
You can observe the JSON-AI if you run the command print(default_json.to_json())
. Given there are many input features, we won't print it out.
These are the only elements required to get off the ground with JSON-AI. However, we're interested in making a custom approach. So, let's make this syntax a file, and introduce our own changes.
with open("default.json", "w") as fp:
fp.write(default_json.to_json())
For Lightwood, the goal of a splitter is to intake an initial dataset (pre-processed ideally, although you can run the pre-processor on each DataFrame within the splitter) and return a dictionary with the keys "train", "test", and "dev" (at minimum). Subsequent steps of the pipeline expect the keys "train", "test", and "dev", so it's important you assign datasets to these as necessary.
We're going to introduce SMOTE sampling in our splitter. SMOTE allows you to quickly learn an approximation to make extra "samples" that mimic the undersampled class.
We will use the package imblearn
and scikit-learn
to quickly create a train/test split and apply SMOTE to our training data only.
NOTE This is simply an example of things you can do with the splitter; whether SMOTE sampling is ideal for your problem depends on the question you're trying to answer!
from lightwood.api.dtype import dtype
import pandas as pd
import numpy as np
from typing import List, Dict
from itertools import product
from lightwood.api.types import TimeseriesSettings
from lightwood.helpers.log import log
from imblearn.over_sampling import SMOTE
from sklearn.model_selection import train_test_split
def MySplitter(
data: pd.DataFrame,
target: str,
pct_train: float = 0.8,
pct_dev: float = 0.1,
seed: int = 1,
) -> Dict[str, pd.DataFrame]:
"""
Custom splitting function
:param data: Input data
:param target: Name of the target
:param pct_train: Percentage of data reserved for training, taken out of full data
:param pct_dev: Percentage of data reserved for dev, taken out of train data
:param seed: Random seed for reproducibility
:returns: A dictionary containing the keys train, test and dev with their respective data frames.
"""
# Shuffle the data
data = data.sample(frac=1, random_state=seed).reset_index(drop=True)
# Split into feature columns + target
X = data.iloc[:, data.columns != target] # .values
y = data[target] # .values
# Create a train/test split
X_train, X_test, y_train, y_test = train_test_split(
X, y, train_size=pct_train, random_state=seed, stratify=data[target]
)
X_train, X_dev, y_train, y_dev = train_test_split(
X, y, test_size=pct_dev, random_state=seed, stratify=y_train
)
# Create a SMOTE model and bump up underbalanced class JUST for train data
SMOTE_model = SMOTE(random_state=seed)
Xtrain_mod, ytrain_mod = SMOTE_model.fit_resample(X_train, y_train.ravel())
Xtrain_mod[target] = ytrain_mod
X_test[target] = y_test
X_dev[target] = y_dev
return {"train": Xtrain_mod, "test": X_test, "dev": X_dev}
~/lightwood_modules
¶We automatically search for custom scripts in your ~/lightwood_modules
path. Place your file there. Later, you'll see when we autogenerate code, that you can change your import location if you choose.
Now let's introduce our custom splitter. JSON-AI keeps a lightweight syntax but fills in many default modules (like splitting, cleaning).
For the custom cleaner, we'll work by editing the "splitter" key. We will change properties within it as follows: (1) "module" - place the name of the function. In our case it will be "MyCustomCleaner.cleaner" (2) "args" - any keyword argument specific to your cleaner's internals.
This will look as follows:
"splitter": {
"module": "MyCustomSplitter.MySplitter",
"args": {
"data": "data",
"target": "$target",
"pct_train": 0.8,
"pct_dev": 0.1,
"seed": 1
}
},
Let's copy our file default.json
into custom.json
and add this block. Then, we can proceed as usual to create python code.
Now we're ready to load up our custom JSON-AI and generate the predictor code!
We can do this by first reading in our custom json-syntax, and then calling the function code_from_json_ai
.
# Make changes to your JSON-file and load the custom version
with open('custom.json', 'r') as fp:
modified_json = JsonAI.from_json(fp.read())
#Generate python code that fills in your pipeline
code = code_from_json_ai(modified_json)
print(code)
# Save code to a file (Optional)
with open('custom_splitter_pipeline.py', 'w') as fp:
fp.write(code)
MyCustomCleaner.py MyCustomCleaner MyCustomSplitter.py MyCustomSplitter import lightwood from lightwood.analysis import * from lightwood.api import * from lightwood.data import * from lightwood.encoder import * from lightwood.ensemble import * from lightwood.helpers.device import * from lightwood.helpers.general import * from lightwood.helpers.log import * from lightwood.helpers.numeric import * from lightwood.helpers.parallelism import * from lightwood.helpers.seed import * from lightwood.helpers.text import * from lightwood.helpers.torch import * from lightwood.mixer import * import pandas as pd from typing import Dict, List import os from types import ModuleType import importlib.machinery import sys for import_dir in [os.path.expanduser("~/lightwood_modules"), "/etc/lightwood_modules"]: if os.path.exists(import_dir) and os.access(import_dir, os.R_OK): for file_name in list(os.walk(import_dir))[0][2]: print(file_name) if file_name[-3:] != ".py": continue mod_name = file_name[:-3] print(mod_name) loader = importlib.machinery.SourceFileLoader( mod_name, os.path.join(import_dir, file_name) ) module = ModuleType(loader.name) loader.exec_module(module) sys.modules[mod_name] = module exec(f"import {mod_name}") class Predictor(PredictorInterface): target: str mixers: List[BaseMixer] encoders: Dict[str, BaseEncoder] ensemble: BaseEnsemble mode: str def __init__(self): seed(420) self.target = "Class" self.mode = "inactive" self.problem_definition = ProblemDefinition.from_dict( { "target": "Class", "pct_invalid": 2, "unbias_target": True, "seconds_per_mixer": 14354, "seconds_per_encoder": 0, "time_aim": 64593.50573948541, "target_weights": None, "positive_domain": False, "timeseries_settings": { "is_timeseries": False, "order_by": None, "window": None, "group_by": None, "use_previous_target": True, "nr_predictions": None, "historical_columns": None, "target_type": "", "allow_incomplete_history": False, }, "anomaly_detection": True, "ignore_features": [], "fit_on_validation": True, "strict_mode": True, "seed_nr": 420, } ) self.accuracy_functions = ["balanced_accuracy_score"] self.identifiers = {} self.dtype_dict = { "Class": "binary", "Time": "integer", "V1": "float", "V2": "float", "V3": "float", "V4": "float", "V5": "float", "V6": "float", "V7": "float", "V8": "float", "V9": "float", "V10": "float", "V11": "float", "V12": "float", "V13": "float", "V14": "float", "V15": "float", "V16": "float", "V17": "float", "V18": "float", "V19": "float", "V20": "float", "V21": "float", "V22": "float", "V23": "float", "V24": "float", "V25": "float", "V26": "float", "V27": "float", "V28": "float", "Amount": "float", } # Any feature-column dependencies self.dependencies = { "Time": [], "V1": [], "V2": [], "V3": [], "V4": [], "V5": [], "V6": [], "V7": [], "V8": [], "V9": [], "V10": [], "V11": [], "V12": [], "V13": [], "V14": [], "V15": [], "V16": [], "V17": [], "V18": [], "V19": [], "V20": [], "V21": [], "V22": [], "V23": [], "V24": [], "V25": [], "V26": [], "V27": [], "V28": [], "Amount": [], } self.input_cols = [ "Time", "V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", "V10", "V11", "V12", "V13", "V14", "V15", "V16", "V17", "V18", "V19", "V20", "V21", "V22", "V23", "V24", "V25", "V26", "V27", "V28", "Amount", ] # Initial stats analysis self.statistical_analysis = None def analyze_data(self, data: pd.DataFrame) -> None: # Perform a statistical analysis on the unprocessed data log.info("Performing statistical analysis on data") self.statistical_analysis = lightwood.data.statistical_analysis( data, self.dtype_dict, {}, self.problem_definition ) # Instantiate post-training evaluation self.analysis_blocks = [ ICP( fixed_significance=None, confidence_normalizer=False, positive_domain=self.statistical_analysis.positive_domain, ), AccStats(deps=["ICP"]), GlobalFeatureImportance(disable_column_importance=False), ] def preprocess(self, data: pd.DataFrame) -> pd.DataFrame: # Preprocess and clean data log.info("Cleaning the data") data = cleaner( data=data, pct_invalid=self.problem_definition.pct_invalid, identifiers=self.identifiers, dtype_dict=self.dtype_dict, target=self.target, mode=self.mode, timeseries_settings=self.problem_definition.timeseries_settings, anomaly_detection=self.problem_definition.anomaly_detection, ) # Time-series blocks return data def split(self, data: pd.DataFrame) -> Dict[str, pd.DataFrame]: # Split the data into training/testing splits log.info("Splitting the data into train/test") train_test_data = MyCustomSplitter.MySplitter( data=data, pct_train=0.8, pct_dev=0.1, seed=1, target=self.target ) return train_test_data def prepare(self, data: Dict[str, pd.DataFrame]) -> None: # Prepare encoders to featurize data self.mode = "train" if self.statistical_analysis is None: raise Exception("Please run analyze_data first") # Column to encoder mapping self.encoders = { "Class": Binary.BinaryEncoder( is_target=True, target_class_distribution=self.statistical_analysis.target_class_distribution, ), "Time": Integer.NumericEncoder(), "V1": Float.NumericEncoder(), "V2": Float.NumericEncoder(), "V3": Float.NumericEncoder(), "V4": Float.NumericEncoder(), "V5": Float.NumericEncoder(), "V6": Float.NumericEncoder(), "V7": Float.NumericEncoder(), "V8": Float.NumericEncoder(), "V9": Float.NumericEncoder(), "V10": Float.NumericEncoder(), "V11": Float.NumericEncoder(), "V12": Float.NumericEncoder(), "V13": Float.NumericEncoder(), "V14": Float.NumericEncoder(), "V15": Float.NumericEncoder(), "V16": Float.NumericEncoder(), "V17": Float.NumericEncoder(), "V18": Float.NumericEncoder(), "V19": Float.NumericEncoder(), "V20": Float.NumericEncoder(), "V21": Float.NumericEncoder(), "V22": Float.NumericEncoder(), "V23": Float.NumericEncoder(), "V24": Float.NumericEncoder(), "V25": Float.NumericEncoder(), "V26": Float.NumericEncoder(), "V27": Float.NumericEncoder(), "V28": Float.NumericEncoder(), "Amount": Float.NumericEncoder(), } # Prepare the training + dev data concatenated_train_dev = pd.concat([data["train"], data["dev"]]) log.info("Preparing the encoders") encoder_prepping_dict = {} # Prepare encoders that do not require learned strategies for col_name, encoder in self.encoders.items(): if not encoder.is_trainable_encoder: encoder_prepping_dict[col_name] = [ encoder, concatenated_train_dev[col_name], "prepare", ] log.info( f"Encoder prepping dict length of: {len(encoder_prepping_dict)}" ) # Setup parallelization parallel_prepped_encoders = mut_method_call(encoder_prepping_dict) for col_name, encoder in parallel_prepped_encoders.items(): self.encoders[col_name] = encoder # Prepare the target if self.target not in parallel_prepped_encoders: if self.encoders[self.target].is_trainable_encoder: self.encoders[self.target].prepare( data["train"][self.target], data["dev"][self.target] ) else: self.encoders[self.target].prepare( pd.concat([data["train"], data["dev"]])[self.target] ) # Prepare any non-target encoders that are learned for col_name, encoder in self.encoders.items(): if encoder.is_trainable_encoder: priming_data = pd.concat([data["train"], data["dev"]]) kwargs = {} if self.dependencies[col_name]: kwargs["dependency_data"] = {} for col in self.dependencies[col_name]: kwargs["dependency_data"][col] = { "original_type": self.dtype_dict[col], "data": priming_data[col], } # If an encoder representation requires the target, provide priming data if hasattr(encoder, "uses_target"): kwargs["encoded_target_values"] = parallel_prepped_encoders[ self.target ].encode(priming_data[self.target]) encoder.prepare( data["train"][col_name], data["dev"][col_name], **kwargs ) def featurize(self, split_data: Dict[str, pd.DataFrame]): # Featurize data into numerical representations for models log.info("Featurizing the data") feature_data = {key: None for key in split_data.keys()} for key, data in split_data.items(): feature_data[key] = EncodedDs(self.encoders, data, self.target) return feature_data def fit(self, enc_data: Dict[str, pd.DataFrame]) -> None: # Fit predictors to estimate target self.mode = "train" # --------------- # # Extract data # --------------- # # Extract the featurized data into train/dev/test encoded_train_data = enc_data["train"] encoded_dev_data = enc_data["dev"] encoded_test_data = enc_data["test"] log.info("Training the mixers") # --------------- # # Fit Models # --------------- # # Assign list of mixers self.mixers = [ Neural( fit_on_dev=True, search_hyperparameters=True, net="DefaultNet", stop_after=self.problem_definition.seconds_per_mixer, target_encoder=self.encoders[self.target], target=self.target, dtype_dict=self.dtype_dict, input_cols=self.input_cols, timeseries_settings=self.problem_definition.timeseries_settings, ), LightGBM( fit_on_dev=True, stop_after=self.problem_definition.seconds_per_mixer, target=self.target, dtype_dict=self.dtype_dict, input_cols=self.input_cols, ), Regression( stop_after=self.problem_definition.seconds_per_mixer, target=self.target, dtype_dict=self.dtype_dict, target_encoder=self.encoders[self.target], ), ] # Train mixers trained_mixers = [] for mixer in self.mixers: try: mixer.fit(encoded_train_data, encoded_dev_data) trained_mixers.append(mixer) except Exception as e: log.warning(f"Exception: {e} when training mixer: {mixer}") if True and mixer.stable: raise e # Update mixers to trained versions self.mixers = trained_mixers # --------------- # # Create Ensembles # --------------- # log.info("Ensembling the mixer") # Create an ensemble of mixers to identify best performing model self.pred_args = PredictionArguments() self.ensemble = BestOf( ts_analysis=None, data=encoded_test_data, accuracy_functions=self.accuracy_functions, target=self.target, mixers=self.mixers, ) self.supports_proba = self.ensemble.supports_proba def analyze_ensemble(self, enc_data: Dict[str, pd.DataFrame]) -> None: # Evaluate quality of fit for the ensemble of mixers # --------------- # # Extract data # --------------- # # Extract the featurized data into train/dev/test encoded_train_data = enc_data["train"] encoded_dev_data = enc_data["dev"] encoded_test_data = enc_data["test"] # --------------- # # Analyze Ensembles # --------------- # log.info("Analyzing the ensemble of mixers") self.model_analysis, self.runtime_analyzer = model_analyzer( data=encoded_test_data, train_data=encoded_train_data, stats_info=self.statistical_analysis, ts_cfg=self.problem_definition.timeseries_settings, accuracy_functions=self.accuracy_functions, predictor=self.ensemble, target=self.target, dtype_dict=self.dtype_dict, analysis_blocks=self.analysis_blocks, ) def learn(self, data: pd.DataFrame) -> None: log.info(f"Dropping features: {self.problem_definition.ignore_features}") data = data.drop( columns=self.problem_definition.ignore_features, errors="ignore" ) self.mode = "train" # Perform stats analysis self.analyze_data(data) # Pre-process the data clean_data = self.preprocess(data) # Create train/test (dev) split train_dev_test = self.split(clean_data) # Prepare encoders self.prepare(train_dev_test) # Create feature vectors from data enc_train_test = self.featurize(train_dev_test) # Prepare mixers self.fit(enc_train_test) # Analyze the ensemble self.analyze_ensemble(enc_train_test) # ------------------------ # # Enable model partial fit AFTER it is trained and evaluated for performance with the appropriate train/dev/test splits. # This assumes the predictor could continuously evolve, hence including reserved testing data may improve predictions. # SET `json_ai.problem_definition.fit_on_validation=False` TO TURN THIS BLOCK OFF. # Update the mixers with partial fit if self.problem_definition.fit_on_validation: log.info("Adjustment on validation requested.") update_data = { "new": enc_train_test["test"], "old": ConcatedEncodedDs( [enc_train_test["train"], enc_train_test["dev"]] ), } # noqa self.adjust(update_data) def adjust(self, new_data: Dict[str, pd.DataFrame]) -> None: # Update mixers with new information self.mode = "train" # --------------- # # Extract data # --------------- # # Extract the featurized data encoded_old_data = new_data["old"] encoded_new_data = new_data["new"] # --------------- # # Adjust (Update) Mixers # --------------- # log.info("Updating the mixers") for mixer in self.mixers: mixer.partial_fit(encoded_new_data, encoded_old_data) def predict(self, data: pd.DataFrame, args: Dict = {}) -> pd.DataFrame: # Remove columns that user specifies to ignore log.info(f"Dropping features: {self.problem_definition.ignore_features}") data = data.drop( columns=self.problem_definition.ignore_features, errors="ignore" ) for col in self.input_cols: if col not in data.columns: data[col] = [None] * len(data) # Clean the data self.mode = "predict" log.info("Cleaning the data") data = cleaner( data=data, pct_invalid=self.problem_definition.pct_invalid, identifiers=self.identifiers, dtype_dict=self.dtype_dict, target=self.target, mode=self.mode, timeseries_settings=self.problem_definition.timeseries_settings, anomaly_detection=self.problem_definition.anomaly_detection, ) # Featurize the data encoded_ds = EncodedDs(self.encoders, data, self.target) encoded_data = encoded_ds.get_encoded_data(include_target=False) self.pred_args = PredictionArguments.from_dict(args) df = self.ensemble(encoded_ds, args=self.pred_args) if self.pred_args.all_mixers: return df else: insights, global_insights = explain( data=data, encoded_data=encoded_data, predictions=df, ts_analysis=None, timeseries_settings=self.problem_definition.timeseries_settings, positive_domain=self.statistical_analysis.positive_domain, anomaly_detection=self.problem_definition.anomaly_detection, analysis=self.runtime_analyzer, target_name=self.target, target_dtype=self.dtype_dict[self.target], explainer_blocks=self.analysis_blocks, fixed_confidence=self.pred_args.fixed_confidence, anomaly_error_rate=self.pred_args.anomaly_error_rate, anomaly_cooldown=self.pred_args.anomaly_cooldown, ) return insights
As you can see, an end-to-end pipeline of our entire ML procedure has been generating. There are several abstracted functions to enable transparency as to what processes your data goes through in order to build these models.
The key steps of the pipeline are as follows:
(1) Run a statistical analysis with analyze_data
(2) Clean your data with preprocess
(3) Make a training/dev/testing split with split
(4) Prepare your feature-engineering pipelines with prepare
(5) Create your features with featurize
(6) Fit your predictor models with fit
You can customize this further if necessary, but you have all the steps necessary to train a model!
We recommend familiarizing with these steps by calling the above commands, ideally in order. Some commands (namely prepare
, featurize
, and fit
) do depend on other steps.
If you want to omit the individual steps, we recommend your simply call the learn
method, which compiles all the necessary steps implemented to give your fully trained predictive models starting with unprocessed data!
Once we have code, we can turn this into a python object by calling predictor_from_code
. This instantiates the PredictorInterface
object.
This predictor object can be then used to run your pipeline.
# Turn the code above into a predictor object
predictor = predictor_from_code(code)
MyCustomCleaner.py MyCustomCleaner MyCustomSplitter.py MyCustomSplitter
# Pre-process the data
cleaned_data = predictor.preprocess(data)
train_test_data = predictor.split(cleaned_data)
INFO:lightwood-51500:Cleaning the data INFO:lightwood-51500:Splitting the data into train/test /home/natasha/lightwood_modules/MyCustomSplitter.py:56: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy X_test[target] = y_test /home/natasha/lightwood_modules/MyCustomSplitter.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy X_dev[target] = y_dev
plt.rcParams['font.size']=15
f = plt.figure(figsize=(18, 5))
ax = f.add_subplot(1,3,1)
ax.hist(train_test_data["train"]['Class'], bins = [-0.1, 0.1, 0.9, 1.1], log=True)
ax.set_ylabel("Log Counts")
ax.set_xticks([0, 1])
ax.set_xticklabels(["0", "1"])
ax.set_xlabel("Class")
ax.set_title("Train:\nDistribution of Classes")
ax.set_ylim([1, 1e6])
ax = f.add_subplot(1,3,2)
ax.hist(train_test_data["dev"]['Class'], bins = [-0.1, 0.1, 0.9, 1.1], log=True, color='k')
ax.set_ylabel("Log Counts")
ax.set_xticks([0, 1])
ax.set_xticklabels(["0", "1"])
ax.set_xlabel("Class")
ax.set_title("Dev:\nDistribution of Classes")
ax.set_ylim([1, 1e6])
ax = f.add_subplot(1,3,3)
ax.hist(train_test_data["test"]['Class'], bins = [-0.1, 0.1, 0.9, 1.1], log=True, color='r')
ax.set_ylabel("Log Counts")
ax.set_xticks([0, 1])
ax.set_xticklabels(["0", "1"])
ax.set_xlabel("Class")
ax.set_title("Test:\nDistribution of Classes")
ax.set_ylim([1, 1e6])
f.tight_layout()
As you can see, our splitter has greatly increased the representation of the minority class within the training data, but not so for the testing or dev data.
We hope this tutorial was informative on how to introduce a custom splitter method to your datasets! For more customization tutorials, please check our documentation.
If you want to download the Jupyter-notebook version of this tutorial, check out the source github location found here: lightwood/docssrc/source/tutorials/custom_splitter
.