def test_list_feature(self):
t = vw.VWClassifier(target="target", num_iterations=10)
try:
df = pd.DataFrame.from_dict([{
"target": "1",
"df": ["a", "b", "c", "d"]
}, {
"target": "2",
"df": ["x", "y", "z"]
}])
t.fit(df)
df2 = pd.DataFrame.from_dict([{
"df": ["a", "b", "c", "d"]
}, {
"df": ["x", "y", "z"]
}])
scores = t.predict_proba(df2)
if not scores is None:
print scores
self.assertEquals(scores.shape[0], 2)
self.assertTrue(scores[0][0] > scores[0][1])
self.assertEquals(scores.shape[1], 2)
self.assertTrue(scores[1][0] < scores[1][1])
finally:
t.close()
def test_zero_based_target(self):
t = vw.VWClassifier(target="target", target_readable="name")
try:
df = pd.DataFrame.from_dict([{
"target": 0,
"b": "c d",
"c": 3,
"name": "zeroTarget"
}, {
"target": 1,
"b": "word2",
"name": "oneTarget"
}])
t.fit(df)
scores = t.predict_proba(df)
print "scores->", scores
self.assertEquals(scores.shape[0], 2)
self.assertEquals(scores.shape[1], 2)
idMap = t.get_class_id_map()
print idMap
formatted_recs_list = []
for index, proba in enumerate(scores[0]):
print index, proba
if index in idMap:
indexName = idMap[index]
else:
indexName = str(index)
formatted_recs_list.append({
"prediction": str(proba),
"predictedClass": indexName,
"confidence": str(proba)
})
print formatted_recs_list
finally:
t.close()
def test_vw_same_score_bug(self):
t = vw.VWClassifier(target="target", num_iterations=10)
try:
df = pd.DataFrame.from_dict([{
"target": "1",
"df": ["a", "b", "c", "d"]
}, {
"target": "2",
"df": ["x", "y", "z"]
}])
t.fit(df)
df2 = pd.DataFrame.from_dict([{
"df": ["a", "b", "c", "d"]
}, {
"df": ["x", "y", "z"]
}])
scores = t.predict_proba(df2)
score_00 = scores[0][0]
score_10 = scores[1][0]
for i in range(1, 4):
scores = t.predict_proba(df2)
self.assertEquals(scores[0][0], score_00)
self.assertEquals(scores[1][0], score_10)
finally:
t.close()
def test_sklearn_pipeline(self):
t = vw.VWClassifier(target="target")
f1 = {"target": 0, "b": 1.0, "c": 0}
f2 = {"target": 1, "b": 0, "c": 2.0}
fs = []
for i in range(1, 50):
fs.append(f1)
fs.append(f2)
print "features=>", fs
df = pd.DataFrame.from_dict(fs)
estimators = [("vw", t)]
p = Pipeline(estimators)
print "fitting"
p.fit(df)
print "get preds 1 "
preds = p.predict_proba(df)
print preds
print "-------------------"
t.close()
# vw sometimes need some more time between invocations. Need to look into this.
import time
time.sleep(5)
joblib.dump(p, "/tmp/pipeline/p")
p2 = joblib.load("/tmp/pipeline/p")
print "get preds 2"
df3 = p2.predict_proba(df)
print df3
vw2 = p2._final_estimator
vw2.close()
def test_numpy_input(self):
t = vw.VWClassifier()
try:
X = np.random.randn(6, 4)
y = np.array([1, 2, 1, 1, 2, 2])
t.fit(X, y)
scores = t.predict_proba(X)
print scores
finally:
t.close()
def test_dict_feature(self):
t = vw.VWClassifier(target="target")
try:
df = pd.DataFrame.from_dict([{"target":"1","df":{"1":0.234,"2":0.1}},{"target":"2","df":{"1":0.5}}])
t.fit(df)
scores = t.predict_proba(df)
print scores
self.assertEquals(scores.shape[0],2)
self.assertEquals(scores.shape[1],2)
finally:
t.close()
def test_predictions(self):
t = vw.VWClassifier(target="target")
try:
df = pd.DataFrame.from_dict([{"target":"1","b":"c d","c":3},{"target":"2","b":"word2"}])
t.fit(df)
preds = t.predict(df)
print preds
self.assertEquals(preds[0],0)
self.assertEquals(preds[1],1)
finally:
t.close()
def test_create_features(self):
t = vw.VWClassifier(target="target")
try:
df = pd.DataFrame.from_dict([{"target":"1","b":"c d","c":3},{"target":"2","b":"word2"}])
t.fit(df)
scores = t.predict_proba(df)
print scores
self.assertEquals(scores.shape[0],2)
self.assertEquals(scores.shape[1],2)
finally:
t.close()
def test_vw_args(self):
t = vw.VWClassifier(target="target",b=18)
try:
f = {}
f2 = {}
for i in range(1,5000):
f[i] = 1
f2[i] = 0.1
df = pd.DataFrame.from_dict([{"target":"1","df":f},{"target":"2","df":f2}])
t.fit(df)
scores = t.predict_proba(df)
print scores
self.assertEquals(scores.shape[0],2)
self.assertEquals(scores.shape[1],2)
finally:
t.close()
