Fix autologging SSL error (mlflow#1741)

## What changes are proposed in this pull request?
 
Master is currently broken due to an SSL error in the TensorFlow autologging test (which uses a google-hosted csv file)
 
## How is this patch tested?
 
(Details)
 
## Release Notes
 
### Is this a user-facing change? 

- [X] No. You can skip the rest of this section.
- [ ] Yes. Give a description of this change to be included in the release notes for MLflow users.
 
(Details in 1-2 sentences. You can just refer to another PR with a description if this PR is part of a larger change.)
 
### What component(s) does this PR affect?
 
- [ ] UI
- [ ] CLI 
- [ ] API 
- [ ] REST-API 
- [ ] Examples 
- [ ] Docs
- [ ] Tracking
- [ ] Projects 
- [ ] Artifacts 
- [ ] Models 
- [ ] Scoring 
- [ ] Serving
- [ ] R
- [ ] Java
- [ ] Python

### How should the PR be classified in the release notes? Choose one:
 
- [ ] `rn/breaking-change` - The PR will be mentioned in the "Breaking Changes" section
- [X] `rn/none` - No description will be included. The PR will be mentioned only by the PR number in the "Small Bugfixes and Documentation Updates" section
- [ ] `rn/feature` - A new user-facing feature worth mentioning in the release notes
- [ ] `rn/bug-fix` - A user-facing bug fix worth mentioning in the release notes
- [ ] `rn/documentation` - A user-facing documentation change worth mentioning in the release notes

tests/tensorflow_autolog/iris_test.csv

@@ -0,0 +1,31 @@
+30,4,setosa,versicolor,virginica
+5.9,3.0,4.2,1.5,1
+6.9,3.1,5.4,2.1,2
+5.1,3.3,1.7,0.5,0
+6.0,3.4,4.5,1.6,1
+5.5,2.5,4.0,1.3,1
+6.2,2.9,4.3,1.3,1
+5.5,4.2,1.4,0.2,0
+6.3,2.8,5.1,1.5,2
+5.6,3.0,4.1,1.3,1
+6.7,2.5,5.8,1.8,2
+7.1,3.0,5.9,2.1,2
+4.3,3.0,1.1,0.1,0
+5.6,2.8,4.9,2.0,2
+5.5,2.3,4.0,1.3,1
+6.0,2.2,4.0,1.0,1
+5.1,3.5,1.4,0.2,0
+5.7,2.6,3.5,1.0,1
+4.8,3.4,1.9,0.2,0
+5.1,3.4,1.5,0.2,0
+5.7,2.5,5.0,2.0,2
+5.4,3.4,1.7,0.2,0
+5.6,3.0,4.5,1.5,1
+6.3,2.9,5.6,1.8,2
+6.3,2.5,4.9,1.5,1
+5.8,2.7,3.9,1.2,1
+6.1,3.0,4.6,1.4,1
+5.2,4.1,1.5,0.1,0
+6.7,3.1,4.7,1.5,1
+6.7,3.3,5.7,2.5,2
+6.4,2.9,4.3,1.3,1

tests/tensorflow_autolog/iris_training.csv

@@ -0,0 +1,121 @@
+120,4,setosa,versicolor,virginica
+6.4,2.8,5.6,2.2,2
+5.0,2.3,3.3,1.0,1
+4.9,2.5,4.5,1.7,2
+4.9,3.1,1.5,0.1,0
+5.7,3.8,1.7,0.3,0
+4.4,3.2,1.3,0.2,0
+5.4,3.4,1.5,0.4,0
+6.9,3.1,5.1,2.3,2
+6.7,3.1,4.4,1.4,1
+5.1,3.7,1.5,0.4,0
+5.2,2.7,3.9,1.4,1
+6.9,3.1,4.9,1.5,1
+5.8,4.0,1.2,0.2,0
+5.4,3.9,1.7,0.4,0
+7.7,3.8,6.7,2.2,2
+6.3,3.3,4.7,1.6,1
+6.8,3.2,5.9,2.3,2
+7.6,3.0,6.6,2.1,2
+6.4,3.2,5.3,2.3,2
+5.7,4.4,1.5,0.4,0
+6.7,3.3,5.7,2.1,2
+6.4,2.8,5.6,2.1,2
+5.4,3.9,1.3,0.4,0
+6.1,2.6,5.6,1.4,2
+7.2,3.0,5.8,1.6,2
+5.2,3.5,1.5,0.2,0
+5.8,2.6,4.0,1.2,1
+5.9,3.0,5.1,1.8,2
+5.4,3.0,4.5,1.5,1
+6.7,3.0,5.0,1.7,1
+6.3,2.3,4.4,1.3,1
+5.1,2.5,3.0,1.1,1
+6.4,3.2,4.5,1.5,1
+6.8,3.0,5.5,2.1,2
+6.2,2.8,4.8,1.8,2
+6.9,3.2,5.7,2.3,2
+6.5,3.2,5.1,2.0,2
+5.8,2.8,5.1,2.4,2
+5.1,3.8,1.5,0.3,0
+4.8,3.0,1.4,0.3,0
+7.9,3.8,6.4,2.0,2
+5.8,2.7,5.1,1.9,2
+6.7,3.0,5.2,2.3,2
+5.1,3.8,1.9,0.4,0
+4.7,3.2,1.6,0.2,0
+6.0,2.2,5.0,1.5,2
+4.8,3.4,1.6,0.2,0
+7.7,2.6,6.9,2.3,2
+4.6,3.6,1.0,0.2,0
+7.2,3.2,6.0,1.8,2
+5.0,3.3,1.4,0.2,0
+6.6,3.0,4.4,1.4,1
+6.1,2.8,4.0,1.3,1
+5.0,3.2,1.2,0.2,0
+7.0,3.2,4.7,1.4,1
+6.0,3.0,4.8,1.8,2
+7.4,2.8,6.1,1.9,2
+5.8,2.7,5.1,1.9,2
+6.2,3.4,5.4,2.3,2
+5.0,2.0,3.5,1.0,1
+5.6,2.5,3.9,1.1,1
+6.7,3.1,5.6,2.4,2
+6.3,2.5,5.0,1.9,2
+6.4,3.1,5.5,1.8,2
+6.2,2.2,4.5,1.5,1
+7.3,2.9,6.3,1.8,2
+4.4,3.0,1.3,0.2,0
+7.2,3.6,6.1,2.5,2
+6.5,3.0,5.5,1.8,2
+5.0,3.4,1.5,0.2,0
+4.7,3.2,1.3,0.2,0
+6.6,2.9,4.6,1.3,1
+5.5,3.5,1.3,0.2,0
+7.7,3.0,6.1,2.3,2
+6.1,3.0,4.9,1.8,2
+4.9,3.1,1.5,0.1,0
+5.5,2.4,3.8,1.1,1
+5.7,2.9,4.2,1.3,1
+6.0,2.9,4.5,1.5,1
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+5.4,3.7,1.5,0.2,0
+6.1,2.9,4.7,1.4,1
+6.5,2.8,4.6,1.5,1
+5.6,2.7,4.2,1.3,1
+6.3,3.4,5.6,2.4,2
+4.9,3.1,1.5,0.1,0
+6.8,2.8,4.8,1.4,1
+5.7,2.8,4.5,1.3,1
+6.0,2.7,5.1,1.6,1
+5.0,3.5,1.3,0.3,0
+6.5,3.0,5.2,2.0,2
+6.1,2.8,4.7,1.2,1
+5.1,3.5,1.4,0.3,0
+4.6,3.1,1.5,0.2,0
+6.5,3.0,5.8,2.2,2
+4.6,3.4,1.4,0.3,0
+4.6,3.2,1.4,0.2,0
+7.7,2.8,6.7,2.0,2
+5.9,3.2,4.8,1.8,1
+5.1,3.8,1.6,0.2,0
+4.9,3.0,1.4,0.2,0
+4.9,2.4,3.3,1.0,1
+4.5,2.3,1.3,0.3,0
+5.8,2.7,4.1,1.0,1
+5.0,3.4,1.6,0.4,0
+5.2,3.4,1.4,0.2,0
+5.3,3.7,1.5,0.2,0
+5.0,3.6,1.4,0.2,0
+5.6,2.9,3.6,1.3,1
+4.8,3.1,1.6,0.2,0
+6.3,2.7,4.9,1.8,2
+5.7,2.8,4.1,1.3,1
+5.0,3.0,1.6,0.2,0
+6.3,3.3,6.0,2.5,2
+5.0,3.5,1.6,0.6,0
+5.5,2.6,4.4,1.2,1
+5.7,3.0,4.2,1.2,1
+4.4,2.9,1.4,0.2,0
+4.8,3.0,1.4,0.1,0
+5.5,2.4,3.7,1.0,1

tests/tensorflow_autolog/test_tensorflow_autolog.py

@@ -16,6 +16,8 @@
 import mlflow.tensorflow
 import mlflow.keras
 
+import os
+
 SavedModelInfo = collections.namedtuple(
         "SavedModelInfo",
         ["path", "meta_graph_tags", "signature_def_key", "inference_df", "expected_results_df"])
@@ -126,15 +128,10 @@ def tf_estimator_random_data_run():
         CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
         SPECIES = ['Setosa', 'Versicolor', 'Virginica']
 
-        train_path = tf.keras.utils.get_file(
-            "iris_training.csv", "https://storage.googleapis.com/download"
-                                 ".tensorflow.org/data/iris_training.csv")
-        test_path = tf.keras.utils.get_file(
-            "iris_test.csv", "https://storage.googleapis.com/download"
-                             ".tensorflow.org/data/iris_test.csv")
-
-        train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
-        test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
+        train = pd.read_csv(os.path.join(os.path.dirname(__file__), "iris_training.csv"),
+                            names=CSV_COLUMN_NAMES, header=0)
+        test = pd.read_csv(os.path.join(os.path.dirname(__file__), "iris_test.csv"),
+                           names=CSV_COLUMN_NAMES, header=0)
 
         train_y = train.pop('Species')
         test_y = test.pop('Species')