pandas-ml-utils-0.0.6.tar.gz

 # pandas-ml-utils **A note of caution**: this is a one man show hobby project in pre-alpha state mainly serving my own needs. Be my guest and use it or extend it. I was really sick of converting data frames to numpy arrays back and forth just to try out a simple logistic regression. So I have started a pandas ml utilities library where everything should be reachable from the data frame itself. Check out the following examples to see what I mean by that. ## Fitting ### Ordinary Binary Classification ```python import pandas as pd import pandas_ml_utils as pmu from sklearn.datasets import load_breast_cancer from sklearn.linear_model import LogisticRegression bc = load_breast_cancer() df = pd.DataFrame(bc.data, columns = bc.feature_names) df["label"] = bc.target fit = df.fit_classifier(pmu.SkitModel(LogisticRegression(solver='lbfgs', max_iter=300), pmu.FeaturesAndLabels(features=['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'worst concave points', 'worst fractal dimension'], labels=['label'])), test_size=0.4) ``` As a result you get a Fit object which holds the fitted model and two ClassificationSummary. One for the training data and one for the test Data. In case of the classification was executed in a notebook you get a nice table:  ### Binary Classification with Loss As you can see in the above example are two confusion matrices the regular well known one and a "loss". The intend of loss matrix is to tell you if a miss classification has a cost i.e. a loss in dollars. ```python import pandas as pd import pandas_ml_utils as pmu from sklearn.linear_model import LogisticRegression df = pd.fetch_yahoo(spy='SPY') df["label"] = df["spy_Close"] > df["spy_Open"] df["loss"] = (df["spy_Open"] / df["spy_Close"] - 1) * 100 fit = df.fit_classifier(pmu.SkitModel(LogisticRegression(solver='lbfgs'), pmu.FeaturesAndLabels(features=['spy_Open', 'spy_Low'], labels=['label'], loss_column='loss')), test_size=0.4) ```  Now you can see the loss in % of dollars of your miss classification. The classification probabilities are plotted on the very top of the plot. ### Autoregressive Models and RNN Shape It is also possible to use the FeaturesAndLabels object to generate autoregressive features. By default lagging features results in an RNN shaped 3D array (in the format as Keras likes it). However we can also use SkitModels the features will be implicitly transformed back into a 2D array (by using the `reshape_rnn_as_ar` function). ```python import pandas_ml_utils as pmu pmu.FeaturesAndLabels(features=['feature'], labels=['label'], feature_lags=range(0, 10)) ``` One may like to use very long lags i.e. to catch seasonal effects. Since very long lags are a bit fuzzy I usually like to smooth them a bit by using simple averages. ```python import pandas_ml_utils as pmu pmu.FeaturesAndLabels(features=['feature'], labels=['label'], target_columns=['strike'], loss_column='put_loss', feature_lags=[0, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233], lag_smoothing={ 6: lambda df: df.SMA(3, price=df.columns[0]), 35: lambda df: df.SMA(5, price=df.columns[0]) }) ``` Every lag from 6 onwards will be smoothed by a 3 period average, every lag from 35 onwards with a 5 periods moving average. ## Cross Validation It is possible to apply a cross validation algorithm to the training data (after the train test split). In case you only want cross validation pass `test_size=0` Note that the current implementation is just fitting the models on all folds one after the other without any averaging of the validation loss. However the folds can be looped many times which essentially means we invented something like fold epochs. Therefore your fitter epochs can be reduced by division of the number of fold epochs. ```python from sklearn.model_selection import KFold cv = KFold(n_splits = 10) fit = df.fit_classifier(..., SomeModel(epochs=100/10), test_size=0.1 # keep 10% very unseen cross_validation=(10, cv.split), ...) ``` ## Back-Testing a Model todo ... `df.backtest_classifier(...)` ## Save, load reuse a Model To save a model you simply call the save method on the model inside of the fit. ``` fit.model.save('/tmp/foo.model') ``` Loading is as simply as calling load on the Model object. You can immediately apply the model on the dataframe to get back the features along with the classification (which is just another data frame). ```python import pandas as pd import pandas_ml_utils as pmu from sklearn.datasets import load_breast_cancer bc = load_breast_cancer() df = pd.DataFrame(bc.data, columns = bc.feature_names) df.classify(pmu.Model.load('/tmp/foo.model')).tail() ``` NOTE If you have a target level for your binary classifier like all houses cheaper then 50k then you can define this target level to the FeaturesAndLabels object likes so: `FeaturesAndLabels(target_columns=['House Price'])`. This target column is simply fed through to the classified dataframe as target columns. ### Fitting other models then classifiers For non classification tasks use the regressor functions the same way as the classifier functions. * df.fit_regressor(...) * df.backtest_regressor(...) * df.regress(...) ### Other utility objects #### LazyDataFrame Very often I need to do a lot of feature engineering. And very often I do not want to treat averages or other engineering methods as part of the data(frame). For this use case I have added a LazyDataFrame object wrapping around a regular DataFrame where some columns will always be calculated on the fly. Here is an example: ```python import pandas_ml_utils as pmu import pandas as pd import talib df = pd.fetch_yahoo(spy='SPY') ldf = pmu.LazyDataFrame(df, rolling_stddev=lambda x: talib.STDDEV(x['spy_Close'], timeperiod=30) / 100) ldf["rolling_stddev"].tail() # Will always be calculated only the fly ``` #### HashableDataFrame The hashable dataframe is nothing which should be used directly. However this is just a hack to allow caching of feature matrices. With heavy usage of LazyDataFrame and heavily lagging of features for AR models the training data preparation might take a long time. To shorten this time i.e. for hyper parameter tuning a cache is very helpful (but keep in mind this is still kind of a hack). to set the cache size (default is 1) set the following environment variable before import `os.environ["CACHE_FEATUES_AND_LABELS"] = "2"`. And to use the cache simply pass the argument to the fit_classifier method like so:`df.fit_classifier(..., cache_feature_matrix=True)` #### MultiModel TODO describe multi models ... ## TODO * replace hard coded summary objects by a summary provider function * multi model is just another implementation of model * add keras model * add more tests ## Wanna help? * currently I only need binary classification * maybe you want to add a feature for multiple classes * for non classification problems you might want to augment the `Summary` * write some te