Merge branch 'release-0.3.4'

README.md

@@ -39,8 +39,8 @@ environment `text_venv`:
 ### Install from PyPI
 
 1. Install `pxtextmining` and its PyPI dependencies:
-   - `pip3 install pxtextmining==0.3.2`  (Linux & MacOS);
-   - `pip install pxtextmining==0.3.2` (Windows);
+   - `pip3 install pxtextmining==0.3.4`  (Linux & MacOS);
+   - `pip install pxtextmining==0.3.4` (Windows);
 1. We also need to install a couple of 
    [`spaCy`](https://github.com/explosion/spacy-models) models. 
 
@@ -65,15 +65,15 @@ All steps in one go:
    ```
    python3 -m venv text_venv
    source text_venv/bin/activate
-   pip3 install pxtextmining==0.3.2
+   pip3 install pxtextmining==0.3.4
    pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
    pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.3.1/en_core_web_lg-2.3.1.tar.gz
    ```
 1. **Windows**
    ```
    python -m venv text_venv
    text_venv\Scripts\activate
-   pip install pxtextmining==0.3.2
+   pip install pxtextmining==0.3.4
    pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.3.1/en_core_web_sm-2.3.1.tar.gz
    pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.3.1/en_core_web_lg-2.3.1.tar.gz
    ```

build/lib/pxtextmining/factories/factory_model_performance.py

@@ -93,7 +93,7 @@ def factory_model_performance(pipe, x_train, y_train, x_test, y_test,
     # Note that SGDClassifier fits a logistic regression when loss is "log", but a Linear SVM when loss is "hinge".
     # Looking at column "learner" in "tuning results", one cannot tell which of the two models SGD is.
     # Let's make that clear.
-    if 'param_clf__estimator_loss' in tuning_results.columns: # Need statement as models other than SGD don't have loss.
+    if 'param_clf__estimator__loss' in tuning_results.columns: # Need statement as models other than SGD don't have loss.
         learners = []
         for i, j in zip(tuning_results["learner"], tuning_results["param_clf__estimator__loss"]):
             if j == "log":

docs/source/conf.py

@@ -22,7 +22,7 @@
 author = 'Andreas D Soteriades'
 
 # The full version, including alpha/beta/rc tags
-release = '0.3.2'
+release = '0.3.4'
 
 
 # -- General configuration ---------------------------------------------------

pxtextmining/factories/factory_model_performance.py

@@ -93,7 +93,7 @@ def factory_model_performance(pipe, x_train, y_train, x_test, y_test,
     # Note that SGDClassifier fits a logistic regression when loss is "log", but a Linear SVM when loss is "hinge".
     # Looking at column "learner" in "tuning results", one cannot tell which of the two models SGD is.
     # Let's make that clear.
-    if 'param_clf__estimator_loss' in tuning_results.columns: # Need statement as models other than SGD don't have loss.
+    if 'param_clf__estimator__loss' in tuning_results.columns: # Need statement as models other than SGD don't have loss.
         learners = []
         for i, j in zip(tuning_results["learner"], tuning_results["param_clf__estimator__loss"]):
             if j == "log":

requirements.txt

@@ -19,7 +19,7 @@ nltk==3.5
 numpy==1.20.2
 pandas==1.2.3
 pickleshare==0.7.5
-Pillow==8.2.0
+Pillow>=8.3.2
 plac==1.1.3
 preshed==3.0.5
 protobuf==3.17.2

setup.py

@@ -6,7 +6,7 @@
 
 setuptools.setup(
 	name="pxtextmining",
-	version="0.3.2",
+	version="0.3.4",
 	author="Andreas D Soteriades",
 	author_email="andreas.soteriades@nottshc.nhs.uk",
 	description="Text Classification of Patient Experience feedback",
@@ -35,7 +35,7 @@
     "numpy>=1.20.2",
     "pandas==1.2.3", 
     "pickleshare==0.7.5", 
-    "Pillow==8.2.0", 
+    "Pillow>=8.3.2", 
     "plac==1.1.3", 
     "preshed==3.0.5", 
     "protobuf==3.17.2",