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Feature/keras sessions #19

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Merged
merged 9 commits into from
Jan 9, 2020
Merged

Feature/keras sessions #19

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79f63d2
introduce tensorflow session handling to allow multiple keras/tensorf…
somefreestring Jan 4, 2020
dd9c93e
rename features_and_labels.loss to gross_loss to avoid confusion with…
somefreestring Jan 5, 2020
739dffd
enable custom objects for keras model
somefreestring Jan 5, 2020
d1b6ce0
fix custom objects
somefreestring Jan 6, 2020
f8f878b
fix version of tensorflow to 1.* for tensorflow 2 implement a TfKeras…
somefreestring Jan 6, 2020
86c41ea
fix readme, optimize imports
somefreestring Jan 6, 2020
7ec0a75
fix custom objects
somefreestring Jan 7, 2020
aad3dc1
update readme/gitignore
somefreestring Jan 8, 2020
9b8cf8c
enter alpha state
somefreestring Jan 8, 2020
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update readme/gitignore
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@somefreestring
somefreestring committed Jan 8, 2020
commit aad3dc180074a34f8140b09418d1d32e01b3634e
3 changes: 2 additions & 1 deletion .gitignore
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Expand Up @@ -5,4 +5,5 @@ __pycache__/
*log*
*log*/
dist/
_build/
_build/
/TODO.txt
12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -129,13 +129,14 @@ from sklearn.linear_model import LogisticRegression
from pandas_ml_utils.summary.binary_classification_summary import BinaryClassificationSummary

df = pd.read_csv('burritos.csv')
df["with_fires"] = df["Fries"].apply(lambda x: str(x).lower() == "x")
df["price"] = df["Cost"] * -1
df = df[["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling", "Uniformity", "Salsa", "Synergy", "Wrap", "overall", "with_fires", "price"]].dropna()
columns = ["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling", "Uniformity", "Salsa", "Synergy", "Wrap", "overall", "with_fires", "price"]
fit = df.fitpmu.SkitModel(LogisticRegression(solver='lbfgs'),
pmu.FeaturesAndLabels(["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling",
"Uniformity", "Salsa", "Synergy", "Wrap", "overall"],
["with_fires"]),
["with_fires"],
pre_processor=lambda _df: pmu.LazyDataFrame(_df,
with_fires = lambda f: f["Fries"].apply(lambda x: str(x).lower() == "x"),
price = lambda f: f["Cost"] * -1).to_dataframe()[columns].dropna()),
BinaryClassificationSummary)

fit
Expand All @@ -152,11 +153,10 @@ serves the needed columns by your features.
fit.save_model("/tmp/burrito.model")
```

An then just apply the model on the data frame as you would expect it from your data source:

```python
df = pd.read_csv('burritos.csv')
df["price"] = df["Cost"] * -1
df = df[["Tortilla", "Temp", "Meat", "Fillings", "Meat:filling", "Uniformity", "Salsa", "Synergy", "Wrap", "overall", "price"]].dropna()
df.predict(pmu.Model.load("/tmp/burrito.model")).tail()
```

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