Shorten the README

README.md

@@ -32,91 +32,60 @@
 
 Currently, we are supporting Python 3. There are several ways to install Cornac:
 
-  - **From PyPI (you may need a C++ compiler):**
-
-```sh
-pip3 install cornac
-```
-
-  - **From Anaconda:**
-
-```sh
-conda install cornac -c conda-forge
-```
-
-  - **From the GitHub source (for latest updates):**
-
-```sh
-pip3 install Cython
-git clone https://github.com/PreferredAI/cornac.git
-cd cornac
-python3 setup.py install
-```
+- **From PyPI (you may need a C++ compiler):**
+  ```bash
+  pip3 install cornac
+  ```
+
+- **From Anaconda:**
+  ```bash
+  conda install cornac -c conda-forge
+  ```
+
+- **From the GitHub source (for latest updates):**
+  ```bash
+  pip3 install Cython
+  git clone https://github.com/PreferredAI/cornac.git
+  cd cornac
+  python3 setup.py install
+  ```
 
 **Note:** 
 
 Additional dependencies required by models are listed [here](README.md#Models).
 
-Some algorithm implementations use `OpenMP` to support multi-threading. For OSX users, in order to run those algorithms efficiently, you might need to install `gcc` from Homebrew to have an OpenMP compiler:
-
-```sh
+Some algorithm implementations use `OpenMP` to support multi-threading. For Mac OS users, in order to run those algorithms efficiently, you might need to install `gcc` from Homebrew to have an OpenMP compiler:
+```bash
 brew install gcc | brew link gcc
 ```
 
-If you want to utilize your GPUs, you might consider:
-
-  - [TensorFlow installation instructions](https://www.tensorflow.org/install/).
-  - [PyTorch installation instructions](https://pytorch.org/get-started/locally/).
-  - [cuDNN](https://docs.nvidia.com/deeplearning/sdk/cudnn-install/) (for Nvidia GPUs).
-
 ## Getting started: your first Cornac experiment
 
 ![](flow.jpg)
 <p align="center"><i>Flow of an Experiment in Cornac</i></p>
 
-Load the built-in [MovieLens 100K](https://grouplens.org/datasets/movielens/100k/) dataset (will be downloaded if not cached):
-
 ```python
 import cornac
-
-ml_100k = cornac.datasets.movielens.load_feedback(variant="100K")
-```
-
-Split the data based on ratio:
-
-```python
-rs = cornac.eval_methods.RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)
-```
-
-Here we are comparing `Biased MF`, `PMF`, and `BPR`:
-
-```python
-mf = cornac.models.MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)
-pmf = cornac.models.PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123)
-bpr = cornac.models.BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)
-```
-
-Define metrics used to evaluate the models:
-
-```python
-mae = cornac.metrics.MAE()
-rmse = cornac.metrics.RMSE()
-prec = cornac.metrics.Precision(k=10)
-recall = cornac.metrics.Recall(k=10)
-ndcg = cornac.metrics.NDCG(k=10)
-auc = cornac.metrics.AUC()
-mAP = cornac.metrics.MAP()
-```
-
-Put everything together into an experiment and run it:
-
-```python
-cornac.Experiment(
-  eval_method=rs,
-  models=[mf, pmf, bpr],
-  metrics=[mae, rmse, recall, ndcg, auc, mAP],
-  user_based=True
-).run()
+from cornac.eval_methods import RatioSplit
+from cornac.models import MF, PMF, BPR
+from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP
+
+# load the built-in MovieLens 100K and split the data based on ratio
+ml_100k = cornac.datasets.movielens.load_feedback()
+rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)
+
+# initialize models, here we are comparing: Biased MF, PMF, and BPR
+models = [
+    MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123),
+    PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123),
+    BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123),
+]
+
+# define metrics to evaluate the models
+metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]
+
+# put it together in an experiment, voilà!
+cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run()
 ```
 
 **Output:**

examples/first_example.py

@@ -15,40 +15,23 @@
 """Your very first example with Cornac"""
 
 import cornac
+from cornac.eval_methods import RatioSplit
+from cornac.models import MF, PMF, BPR
+from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP
 
-
-# Load MovieLens 100K dataset
+# load the built-in MovieLens 100K and split the data based on ratio
 ml_100k = cornac.datasets.movielens.load_feedback()
+rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)
 
-# Split data based on ratio
-rs = cornac.eval_methods.RatioSplit(
-    data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123
-)
-
-# Here we are comparing biased MF, PMF, and BPR
-mf = cornac.models.MF(
-    k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123
-)
-pmf = cornac.models.PMF(
-    k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123
-)
-bpr = cornac.models.BPR(
-    k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123
-)
+# initialize models, here we are comparing: Biased MF, PMF, and BPR
+models = [
+    MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123),
+    PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123),
+    BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123),
+]
 
-# Define metrics used to evaluate the models
-mae = cornac.metrics.MAE()
-rmse = cornac.metrics.RMSE()
-prec = cornac.metrics.Precision(k=10)
-recall = cornac.metrics.Recall(k=10)
-ndcg = cornac.metrics.NDCG(k=10)
-auc = cornac.metrics.AUC()
-mAP = cornac.metrics.MAP()
+# define metrics to evaluate the models
+metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]
 
-# Put it together into an experiment and run
-cornac.Experiment(
-    eval_method=rs,
-    models=[mf, pmf, bpr],
-    metrics=[mae, rmse, prec, recall, ndcg, auc, mAP],
-    user_based=True,
-).run()
+# put it together in an experiment, voilà!
+cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run()