full refactor

.github/pull_request_template.md

@@ -1,12 +1,12 @@
 ---
 name: b) New feature
-about: Select this if you're adding a new feature to verve.
+about: Select this if you're adding a new feature to libra.
 labels: feature
 ---
 
 <!--
 Please make sure you've read and understood our contributing guidelines;
-https://github.com/Palashio/verve/CONTRIBUTING.md
+https://github.com/Palashio/libra/CONTRIBUTING.md
 -->
 
 This pull request closes #issue_number_here .
@@ -24,4 +24,4 @@ including tests for the new feature you added.
 
 - [ ] I updated the docs.
 
-This pull request adds a new feature to verve. @Palashio, could you please take a look at it?
+This pull request adds a new feature to libra. @Palashio, could you please take a look at it?

CONTRIBUTING.md

@@ -1 +1 @@
-Guidelines for contributing to verve will come soon.
+Guidelines for contributing to libra will come soon.

README.md

@@ -1,15 +1,15 @@
 ## INITIAL BUILD RELEASE DATE: JULY 16TH.
 in progress....this README.md is out of date. Documentation site coming soon. 
 
-# Verve: Deep Learning fluent in one-liners
+# libra: Deep Learning fluent in one-liners
 
-Verve is a deep learning API that allows users to use machine learning in their workflows in fluent one-liners. It is written in Python and TensorFlow and makes training neural networks as simple as a one line function call. It was written to make deep learning as simple as possible to every user. 
+libra is a deep learning API that allows users to use machine learning in their workflows in fluent one-liners. It is written in Python and TensorFlow and makes training neural networks as simple as a one line function call. It was written to make deep learning as simple as possible to every user. 
 *** 
 
 ## Guiding Principles ## 
-  * **Beginner Friendly.** verve is an API designed to be used by developers with no deep learning experience whatsoever. It is built so that users with no knowledge in preprocessing, modeling, or tuning can build high-performance models with ease without worrying about the details of implementation.
+  * **Beginner Friendly.** libra is an API designed to be used by developers with no deep learning experience whatsoever. It is built so that users with no knowledge in preprocessing, modeling, or tuning can build high-performance models with ease without worrying about the details of implementation.
 
-  * **Quick Integration.** With the recent rise of machine learning on the cloud, the developer community has failed to make easy-to-use platforms that exist locally and integrate directly into workflows. verve allows users to develop models directly in programs with hundreds of API endpoints without having to worry about the transition to the cloud.
+  * **Quick Integration.** With the recent rise of machine learning on the cloud, the developer community has failed to make easy-to-use platforms that exist locally and integrate directly into workflows. libra allows users to develop models directly in programs with hundreds of API endpoints without having to worry about the transition to the cloud.
 
   * **Automation.** End-to-end pipelines containing hundreds of processes are automatically run for the user. The developer only has to consider what they want to accomplish from the task and the location of their initial dataset.
 
@@ -54,7 +54,7 @@ Generally, all queries have the same structure. You should always be passing an
 Let's start with the most basic query. This will build a feed-forward network for a continuous label that you specify.
 
 ```python
-import verve
+import libra
 
 newClient = client('dataset')
 newClient.regression_query_ann('Model the median house value')
@@ -235,9 +235,9 @@ If you'd like to get the best pipeline for dimensionality reduction you can call
  dimensionality_reduc("I want to estimate number of crime", path_to_dataset) 
 
 ```
-Instructions like "I want to model x" are provided in the dimensionality reduction pipeline because it identifies which prediction objective you would like to maximize the accuracy for. Providing this instruction helps verve provide users with the best modification pipeline. 
+Instructions like "I want to model x" are provided in the dimensionality reduction pipeline because it identifies which prediction objective you would like to maximize the accuracy for. Providing this instruction helps libra provide users with the best modification pipeline. 
 
-verve current supports feature importance identification using random forest regressor, indepedent component analysis, and principle component analysis. The output of the dimensionalityReduc() function should look something like this: 
+libra current supports feature importance identification using random forest regressor, indepedent component analysis, and principle component analysis. The output of the dimensionalityReduc() function should look something like this: 
 
 ```
 Baseline Accuracy: 0.9752906976744186
@@ -299,7 +299,7 @@ If this does not converge a message will be displayed for users to warn them by
 
 ## Process Logger ##
 
-verve will automatically output the current process running in a hierarchial format like this:
+libra will automatically output the current process running in a hierarchial format like this:
 
 ```
 loading dataset...
@@ -321,9 +321,9 @@ A quiet mode feature is currently being implemented.
 
 ## Pipelining for Contributors ##
 
-In order to help make verve extensible, a process pipeliner has been implemented to help contributors easily test their newly-developed modules. 
+In order to help make libra extensible, a process pipeliner has been implemented to help contributors easily test their newly-developed modules. 
 
-Let's say you've developed a different preprocesser for data that you want to test before integrating it into verve's primary workflow. This is the process to test it out:
+Let's say you've developed a different preprocesser for data that you want to test before integrating it into libra's primary workflow. This is the process to test it out:
 
 First, you want to initialize your base parameters, which are your instructions, the path to your dataset, and any other information your new function might require.
 
@@ -359,7 +359,7 @@ All model information should be stored in ```init_params```. If you'd like to mo
 ***
 ## Instructions ##
 
-verve uses intelligent natural language processing to analyze user instructions and match it with a column in user datasets. 
+libra uses intelligent natural language processing to analyze user instructions and match it with a column in user datasets. 
   1. [Textblob](https://textblob.readthedocs.io/en/dev/), a part of speech recognition algorithm, is used to identify parts of speech.
   2. A self-developed part-of-speech deciphering algorithm is used to extract relevant parts of a sentence. 
   3. Masks are generated to represent all words as tensors in order for easy comparison
@@ -368,4 +368,4 @@ verve uses intelligent natural language processing to analyze user instructions
 
 ## Example Projects ##
 
-To get started, take a look at some of these examples of data science projects analyzing datasets using verve. 
+To get started, take a look at some of these examples of data science projects analyzing datasets using libra. 

docker/Dockerfile

@@ -27,7 +27,7 @@ RUN wget --quiet --no-check-certificate https://repo.continuum.io/miniconda/Mini
     echo export PATH=$CONDA_DIR/bin:'$PATH' > /etc/profile.d/conda.sh
 
 # Install Python packages and keras
-ENV NB_USER verve
+ENV NB_USER libra
 ENV NB_UID 1000
 
 RUN useradd -m -s /bin/bash -N -u $NB_UID $NB_USER && \
@@ -75,7 +75,7 @@ RUN conda install -y python=${python_version} && \
       theano \
       mkdocs \
       && \
-      git clone git://github.com/Palashio/verve.git /src && pip install -e /src[tests] && pip install git+git://github.com/Palashio/verve.git && \
+      git clone git://github.com/Palashio/libra.git /src && pip install -e /src[tests] && pip install git+git://github.com/Palashio/libra.git && \
     conda clean -yt; exit 0
 
 ENV LC_ALL=C.UTF-8

docker/Makefile

@@ -13,16 +13,16 @@ TEST=tests/
 SRC?=$(shell dirname `pwd`)
 
 build:
-	docker build -t verve --build-arg python_version=$(PYTHON_VERSION) --build-arg cuda_version=$(CUDA_VERSION) --build-arg cudnn_version=$(CUDNN_VERSION) -f $(DOCKER_FILE) .
+	docker build -t libra --build-arg python_version=$(PYTHON_VERSION) --build-arg cuda_version=$(CUDA_VERSION) --build-arg cudnn_version=$(CUDNN_VERSION) -f $(DOCKER_FILE) .
 
 bash: build
-	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env verve_BACKEND=$(BACKEND) verve bash
+	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env libra_BACKEND=$(BACKEND) libra bash
 
 ipython: build
-	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env verve_BACKEND=$(BACKEND) verve ipython
+	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env libra_BACKEND=$(BACKEND) libra ipython
 
 notebook: build
-	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --net=host --env verve_BACKEND=$(BACKEND) verve
+	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --net=host --env libra_BACKEND=$(BACKEND) libra
 
 test: build
-	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env verve_BACKEND=$(BACKEND) verve py.test $(TEST)
+	$(DOCKER) run -it -v $(SRC):/src/workspace -v $(DATA):/data --env libra_BACKEND=$(BACKEND) libra py.test $(TEST)

docker/README.md

@@ -1,7 +1,7 @@
-# Using verve via Docker
+# Using libra via Docker
 
 This directory contains `Dockerfile` to make it easy to get up and running with
-verve via [Docker](http://www.docker.com/).
+libra via [Docker](http://www.docker.com/).
 
 ## Installing Docker
 

verve/dev_pipeliner/regression_split_functions.py → libra/dev_pipeliner/regression_split_functions.py

@@ -18,23 +18,23 @@
 from sklearn.preprocessing import OneHotEncoder, StandardScaler
 from tensorflow import keras
 from tensorflow.python.keras.layers import Dense, Input
-from verve.data_generation.dataset_labelmatcher import get_similar_column, get_similar_model
+from libra.data_generation.dataset_labelmatcher import get_similar_column, get_similar_model
 from keras.callbacks import EarlyStopping
 from matplotlib import pyplot
-from verve.data_generation.grammartree import get_value_instruction
-from verve.preprocessing.data_preprocesser import structured_preprocesser
-from verve.modeling.prediction_model_creation import get_keras_model_reg
-from verve.modeling.prediction_model_creation import get_keras_model_class
+from libra.data_generation.grammartree import get_value_instruction
+from libra.preprocessing.data_preprocesser import structured_preprocesser
+from libra.modeling.prediction_model_creation import get_keras_model_reg
+from libra.modeling.prediction_model_creation import get_keras_model_class
 from keras.utils import to_categorical
 from keras.utils import np_utils
 from sklearn.cluster import KMeans
 import matplotlib.pyplot as plt
 
-from verve.data_generation import dataGen
+from libra.data_generation import dataGen
 from keras.models import Sequential
 from keras.layers import (Dense, Conv2D, Flatten)
 from os import listdir
-from verve.modeling.tuner import tuneReg, tuneClass, tuneCNN
+from libra.modeling.tuner import tuneReg, tuneClass, tuneCNN
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.feature_selection import SelectFromModel

verve/preprocessing/NLP_preprocessing.py → libra/preprocessing/NLP_preprocessing.py

@@ -4,8 +4,8 @@
 import spacy
 from spacy.lang.en import English
 from nltk.corpus import stopwords
-from verve.data_generation.dataset_labelmatcher import get_similar_column
-from verve.data_generation.grammartree import get_value_instruction
+from libra.data_generation.dataset_labelmatcher import get_similar_column
+from libra.data_generation.grammartree import get_value_instruction
 
 
 def get_target_values(data, instruction, yLabel):

verve/preprocessing/data_preprocesser.py → libra/preprocessing/data_preprocesser.py

@@ -8,8 +8,8 @@
 from sklearn.preprocessing import (OneHotEncoder,
                                    StandardScaler,
                                    FunctionTransformer)
-from verve.data_generation.dataset_labelmatcher import get_similar_column
-from verve.data_generation.grammartree import get_value_instruction
+from libra.data_generation.dataset_labelmatcher import get_similar_column
+from libra.data_generation.grammartree import get_value_instruction
 from prince.ca import CA
 
 from nltk.stem import WordNetLemmatizer

verve/preprocessing/image_preprocesser.py → libra/preprocessing/image_preprocesser.py

@@ -3,8 +3,8 @@
 import cv2
 import glob
 import pandas as pd
-from verve.data_generation.dataset_labelmatcher import get_similar_column
-from verve.data_generation.grammartree import get_value_instruction
+from libra.data_generation.dataset_labelmatcher import get_similar_column
+from libra.data_generation.grammartree import get_value_instruction
 
 # Preprocesses images from images to median of heighs/widths
 

verve/queries.py → libra/queries.py

@@ -1,20 +1,20 @@
-from verve.query.nlp_queries import (image_caption_query,
+from libra.query.nlp_queries import (image_caption_query,
                                      generate_caption, classify_text,
                                      text_classification_query, get_summary,
                                      summarization_query)
-from verve.query.classification_models import (k_means_clustering,
+from libra.query.classification_models import (k_means_clustering,
                                                train_svm, nearest_neighbors,
                                                decision_tree)
 
-from verve.query.supplementaries import tune_helper, get_model_data, get_operators, get_accuracy, get_losses, get_target, get_plots, get_vocab
+from libra.query.supplementaries import tune_helper, get_model_data, get_operators, get_accuracy, get_losses, get_target, get_plots, get_vocab
 
-from verve.query.feedforward_nn import (regression_ann,
+from libra.query.feedforward_nn import (regression_ann,
                                         classification_ann,
                                         convolutional)
-from verve.data_generation.grammartree import get_value_instruction
-from verve.data_generation.dataset_labelmatcher import (get_similar_column,
+from libra.data_generation.grammartree import get_value_instruction
+from libra.data_generation.dataset_labelmatcher import (get_similar_column,
                                                         get_similar_model)
-from verve.plotting.generate_plots import analyze
+from libra.plotting.generate_plots import analyze
 from colorama import Fore, Style
 import pandas as pd
 from pandas.core.common import SettingWithCopyWarning
@@ -829,4 +829,4 @@ def analyze(self, model=None):
         if model is None:
             model = self.latest_model
         clearLog()
-        analyze(self, model)
+        analyze(self, model)

verve/query/classification_models.py → libra/query/classification_models.py

@@ -1,14 +1,14 @@
 import pandas as pd
-from verve.preprocessing.data_reader import DataReader
+from libra.preprocessing.data_reader import DataReader
 from sklearn import svm, tree
 from sklearn.metrics import accuracy_score
-from verve.preprocessing.data_preprocesser import initial_preprocesser, clustering_preprocessor
+from libra.preprocessing.data_preprocesser import initial_preprocesser, clustering_preprocessor
 from sklearn.cluster import KMeans
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.model_selection import cross_val_score
 import numpy as np
-from verve.query.supplementaries import generate_id
-from verve.plotting.generate_plots import (generate_clustering_plots)
+from libra.query.supplementaries import generate_id
+from libra.plotting.generate_plots import (generate_clustering_plots)
 from colorama import Fore, Style
 import warnings
 

verve/query/dimensionality_red_queries.py → libra/query/dimensionality_red_queries.py

@@ -1,12 +1,12 @@
 from sklearn.feature_selection import SelectFromModel
 from sklearn import preprocessing, tree
 from itertools import product, permutations
-from verve.preprocessing.data_reader import DataReader
+from libra.preprocessing.data_reader import DataReader
 import os
 from sklearn.ensemble import RandomForestRegressor
-from verve.preprocessing.data_preprocesser import structured_preprocesser, initial_preprocesser
-from verve.data_generation.grammartree import get_value_instruction
-from verve.data_generation.dataset_labelmatcher import get_similar_column
+from libra.preprocessing.data_preprocesser import structured_preprocesser, initial_preprocesser
+from libra.data_generation.grammartree import get_value_instruction
+from libra.data_generation.dataset_labelmatcher import get_similar_column
 
 from xgboost import XGBClassifier
 

verve/query/feedforward_nn.py → libra/query/feedforward_nn.py

@@ -1,22 +1,22 @@
 from colorama import Fore, Style
 from tensorflow.keras.callbacks import EarlyStopping
 import os
-from verve.preprocessing.image_preprocesser import (setwise_preprocessing,
+from libra.preprocessing.image_preprocesser import (setwise_preprocessing,
                                                     csv_preprocessing,
                                                     classwise_preprocessing,
                                                     set_distinguisher,
                                                     already_processed)
-from verve.preprocessing.data_reader import DataReader
+from libra.preprocessing.data_reader import DataReader
 from keras.models import Sequential
 from keras.layers import (Dense, Conv2D, Flatten, MaxPooling2D, )
 import pandas as pd
-from verve.query.supplementaries import save, generate_id
+from libra.query.supplementaries import save, generate_id
 from keras.preprocessing.image import ImageDataGenerator
 from sklearn.preprocessing import OneHotEncoder
-from verve.plotting.generate_plots import (generate_regression_plots,
+from libra.plotting.generate_plots import (generate_regression_plots,
                                            generate_classification_plots)
-from verve.preprocessing.data_preprocesser import initial_preprocesser
-from verve.modeling.prediction_model_creation import get_keras_model_reg, get_keras_model_class
+from libra.preprocessing.data_preprocesser import initial_preprocesser
+from libra.modeling.prediction_model_creation import get_keras_model_reg, get_keras_model_class
 from sklearn.preprocessing import StandardScaler
 import numpy as np